Model Analysis
Nikhil Gupta
2019-02-13 08:22:04
sessionInfo()## R version 3.5.1 (2018-07-02)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows 10 x64 (build 17134)
##
## Matrix products: default
##
## locale:
## [1] LC_COLLATE=English_United States.1252
## [2] LC_CTYPE=English_United States.1252
## [3] LC_MONETARY=English_United States.1252
## [4] LC_NUMERIC=C
## [5] LC_TIME=English_United States.1252
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## loaded via a namespace (and not attached):
## [1] compiler_3.5.1 magrittr_1.5 tools_3.5.1 htmltools_0.3.6
## [5] yaml_2.2.0 Rcpp_1.0.0 stringi_1.2.4 rmarkdown_1.11
## [9] knitr_1.20 stringr_1.3.1 digest_0.6.18 evaluate_0.12User Inputs
output.var = params$output.var
transform.abs = FALSE
log.pred = FALSE
norm.pred = FALSE
if (params$trans == 1){
transform.abs == TRUE
}else if (params$trans == 2){
log.pred = TRUE
}else if (params$trans == 3){
norm.pred = TRUE
}else{
message("You have chosen no transformation")
}
eda = params$eda
algo.forward = params$algo.forward
algo.backward = params$algo.backward
algo.stepwise = params$algo.stepwise
algo.LASSO = params$algo.LASSO
algo.LARS = params$algo.LARS
algo.forward.caret = params$algo.forward.caret
algo.backward.caret = params$algo.backward.caret
algo.stepwise.caret = params$algo.stepwise.caret
algo.LASSO.caret = params$algo.LASSO.caret
algo.LARS.caret = params$algo.LARS.caret
message("Parameters used for training/prediction: ")## Parameters used for training/prediction:str(params)## List of 13
## $ output.var : chr "y3"
## $ trans : int 3
## $ eda : logi FALSE
## $ algo.forward : logi FALSE
## $ algo.backward : logi FALSE
## $ algo.stepwise : logi FALSE
## $ algo.LASSO : logi FALSE
## $ algo.LARS : logi FALSE
## $ algo.forward.caret : logi TRUE
## $ algo.backward.caret: logi TRUE
## $ algo.stepwise.caret: logi TRUE
## $ algo.LASSO.caret : logi TRUE
## $ algo.LARS.caret : logi TRUE# Setup Labels
# alt.scale.label.name = Alternate Scale variable name
# - if predicting on log, then alt.scale is normal scale
# - if predicting on normal scale, then alt.scale is log scale
if (log.pred == TRUE){
label.names = paste('log.',output.var,sep="")
alt.scale.label.name = output.var
}
if (log.pred == FALSE & norm.pred==FALSE){
label.names = output.var
alt.scale.label.name = paste('log.',output.var,sep="")
}
if (norm.pred==TRUE){
label.names = paste('norm.',output.var,sep="")
alt.scale.label.name = output.var
}Prepare Data
Read and Clean Features
features = read.csv("../../Data/features.csv")
features.highprec = read.csv("../../Data/features_highprec.csv")all.equal(features, features.highprec)## [1] "Component \"x11\": Mean relative difference: 0.001401482"
## [2] "Component \"stat9\": Mean relative difference: 0.0002946299"
## [3] "Component \"stat12\": Mean relative difference: 0.0005151515"
## [4] "Component \"stat13\": Mean relative difference: 0.001354369"
## [5] "Component \"stat18\": Mean relative difference: 0.0005141104"
## [6] "Component \"stat22\": Mean relative difference: 0.001135977"
## [7] "Component \"stat25\": Mean relative difference: 0.0001884615"
## [8] "Component \"stat29\": Mean relative difference: 0.001083691"
## [9] "Component \"stat36\": Mean relative difference: 0.00021513"
## [10] "Component \"stat37\": Mean relative difference: 0.0004578125"
## [11] "Component \"stat43\": Mean relative difference: 0.0003473684"
## [12] "Component \"stat45\": Mean relative difference: 0.0002951699"
## [13] "Component \"stat46\": Mean relative difference: 0.0009745763"
## [14] "Component \"stat47\": Mean relative difference: 8.829902e-05"
## [15] "Component \"stat55\": Mean relative difference: 0.001438066"
## [16] "Component \"stat57\": Mean relative difference: 0.0001056911"
## [17] "Component \"stat58\": Mean relative difference: 0.0004882261"
## [18] "Component \"stat60\": Mean relative difference: 0.0002408377"
## [19] "Component \"stat62\": Mean relative difference: 0.0004885106"
## [20] "Component \"stat66\": Mean relative difference: 1.73913e-06"
## [21] "Component \"stat67\": Mean relative difference: 0.0006265823"
## [22] "Component \"stat73\": Mean relative difference: 0.003846154"
## [23] "Component \"stat75\": Mean relative difference: 0.002334906"
## [24] "Component \"stat83\": Mean relative difference: 0.0005628415"
## [25] "Component \"stat86\": Mean relative difference: 0.0006104418"
## [26] "Component \"stat94\": Mean relative difference: 0.001005115"
## [27] "Component \"stat97\": Mean relative difference: 0.0003551913"
## [28] "Component \"stat98\": Mean relative difference: 0.0006157635"
## [29] "Component \"stat106\": Mean relative difference: 0.0008267717"
## [30] "Component \"stat109\": Mean relative difference: 0.0005121359"
## [31] "Component \"stat110\": Mean relative difference: 0.0007615527"
## [32] "Component \"stat111\": Mean relative difference: 0.001336134"
## [33] "Component \"stat114\": Mean relative difference: 7.680492e-05"
## [34] "Component \"stat117\": Mean relative difference: 0.0002421784"
## [35] "Component \"stat122\": Mean relative difference: 0.0006521084"
## [36] "Component \"stat123\": Mean relative difference: 8.333333e-05"
## [37] "Component \"stat125\": Mean relative difference: 0.002385135"
## [38] "Component \"stat130\": Mean relative difference: 0.001874016"
## [39] "Component \"stat132\": Mean relative difference: 0.0003193182"
## [40] "Component \"stat135\": Mean relative difference: 0.0001622517"
## [41] "Component \"stat136\": Mean relative difference: 7.722008e-05"
## [42] "Component \"stat138\": Mean relative difference: 0.0009739953"
## [43] "Component \"stat143\": Mean relative difference: 0.0004845361"
## [44] "Component \"stat146\": Mean relative difference: 0.0005821596"
## [45] "Component \"stat148\": Mean relative difference: 0.0005366922"
## [46] "Component \"stat153\": Mean relative difference: 0.0001557522"
## [47] "Component \"stat154\": Mean relative difference: 0.001351916"
## [48] "Component \"stat157\": Mean relative difference: 0.0005427928"
## [49] "Component \"stat162\": Mean relative difference: 0.002622951"
## [50] "Component \"stat167\": Mean relative difference: 0.0005905172"
## [51] "Component \"stat168\": Mean relative difference: 0.0002791096"
## [52] "Component \"stat169\": Mean relative difference: 0.0004121827"
## [53] "Component \"stat170\": Mean relative difference: 0.0004705882"
## [54] "Component \"stat174\": Mean relative difference: 0.0003822894"
## [55] "Component \"stat179\": Mean relative difference: 0.0008286604"
## [56] "Component \"stat184\": Mean relative difference: 0.0007526718"
## [57] "Component \"stat187\": Mean relative difference: 0.0005122768"
## [58] "Component \"stat193\": Mean relative difference: 4.215116e-05"
## [59] "Component \"stat199\": Mean relative difference: 0.002155844"
## [60] "Component \"stat203\": Mean relative difference: 0.0003738318"
## [61] "Component \"stat213\": Mean relative difference: 0.000667676"
## [62] "Component \"stat215\": Mean relative difference: 0.0003997955"head(features)## JobName x1 x2 x3 x4 x5 x6
## 1 Job_00001 2.0734508 4.917267 19.96188 3.520878 7.861051 1.6067589
## 2 Job_00002 2.2682543 4.955773 19.11939 19.763031 6.931355 1.3622041
## 3 Job_00003 1.7424456 2.059819 13.37912 38.829132 6.274053 2.0529845
## 4 Job_00004 0.7873555 2.613983 17.23044 64.402557 5.377652 0.9067419
## 5 Job_00005 2.3342753 4.299076 14.64883 52.537304 6.793368 2.4605792
## 6 Job_00006 1.2365089 2.795370 11.13127 96.819939 6.583971 2.3510606
## x7 x8 x9 x10 x11 x12 x13
## 1 2.979479 8.537228 1.103368 4.6089458 1.05e-07 7.995825 13.215498
## 2 2.388119 6.561461 0.588572 1.0283282 1.03e-07 7.486966 22.557224
## 3 2.043592 10.275595 4.834385 4.3872848 1.06e-07 6.350142 15.049810
## 4 2.395118 13.487331 3.340190 4.5053501 9.47e-08 9.548698 17.170635
## 5 2.891535 9.362389 1.246039 1.7333300 1.01e-07 9.596095 5.794567
## 6 1.247838 7.033354 1.852231 0.4839371 1.07e-07 3.810983 23.863169
## x14 x15 x16 x17 x18 x19 x20
## 1 4.377983 0.2370623 6.075459 3.988347 4.767475 2.698775 1.035893
## 2 2.059315 0.5638121 6.903891 4.152054 6.849232 9.620731 1.915288
## 3 3.260057 2.0603445 8.424065 4.489893 3.493591 4.715386 1.558103
## 4 3.093478 1.8806034 11.189792 2.134271 5.588357 5.107871 1.489588
## 5 3.943076 1.5820830 7.096742 3.563378 7.765610 1.360272 1.240283
## 6 1.280562 1.1733382 7.062051 1.341864 7.748325 5.009365 1.725179
## x21 x22 x23 stat1 stat2 stat3 stat4
## 1 42.36548 1.356213 2.699796 2.3801832 0.1883335 -1.2284011 -0.5999233
## 2 26.63295 4.053961 2.375127 -1.4069480 1.8140973 1.6204884 2.6422672
## 3 20.09693 3.079888 4.488420 -0.7672566 -0.1230289 1.1415752 2.9805934
## 4 32.60415 1.355396 3.402398 0.4371202 -1.9355906 0.9028624 -1.6025400
## 5 44.58361 1.940301 2.249011 2.4492466 -0.6172000 -2.5520642 -2.1485929
## 6 28.75102 2.500499 5.563972 -1.7899084 1.8853619 2.4154840 -2.6022179
## stat5 stat6 stat7 stat8 stat9 stat10
## 1 0.148893163 -0.6622978 -2.4851868 0.3647782 2.5364335 2.92067981
## 2 1.920768980 1.7411555 -1.9599979 -2.0190558 -1.3732762 -0.31642506
## 3 2.422584300 -0.4166040 2.2205689 -2.6741531 0.4844292 2.73379230
## 4 -0.001795933 -0.6946563 -0.3693534 -0.9709467 1.7960306 0.74771154
## 5 -2.311132430 -1.0166832 2.7269876 1.5424492 -1.3156369 -0.09767897
## 6 -1.785491470 -1.8599915 1.4875095 2.0188572 -1.4892503 -1.41103566
## stat11 stat12 stat13 stat14 stat15 stat16
## 1 -2.3228905 -2.480567 -0.6335157 -0.3650149 -0.5322812 0.6029300
## 2 -0.8547903 1.119316 0.7227427 0.2121097 -0.1452281 -2.0361528
## 3 -2.1821580 2.865401 -2.9756081 2.9871745 1.9539525 -1.8857163
## 4 1.3982378 1.856765 -1.0379983 2.3341896 2.3057184 -2.8947697
## 5 0.9567220 2.567549 0.3184886 1.0307668 0.1644241 -0.6613821
## 6 0.5341771 -1.461822 0.4402476 -1.9282095 -0.3680157 1.8188807
## stat17 stat18 stat19 stat20 stat21 stat22
## 1 -1.04516208 2.3544915 2.4049001 0.2633883 -0.9788178 1.7868229
## 2 0.09513074 0.4727738 1.8899702 2.7892542 -1.3919091 -1.7198164
## 3 0.40285346 1.4655282 -1.4952788 2.9162340 -2.3893208 2.8161423
## 4 2.97446084 2.3896182 2.3083484 -1.1894441 -2.1982553 1.3666242
## 5 -0.98465055 0.6900643 1.5894209 -2.1204538 1.7961155 -0.9362189
## 6 -1.45726359 -2.1139548 -0.3964904 1.1764175 -2.9100556 -2.1359294
## stat23 stat24 stat25 stat26 stat27 stat28
## 1 -2.3718851 2.8580718 -0.4719713 -2.817086 -0.9518474 2.88892484
## 2 -2.3293245 1.5577759 -1.9569720 1.554194 -0.5081459 -1.58715141
## 3 -2.5402296 0.1422861 0.3572798 -1.051886 -2.1541717 0.03074004
## 4 -1.9679050 -1.4077642 2.5097435 1.683121 -0.2549745 -2.90384054
## 5 2.0523429 -2.2084844 -1.9280857 -2.116736 1.8180779 -1.42167580
## 6 0.2184991 -0.7599817 2.6880329 -2.903350 -1.0733233 -2.92416644
## stat29 stat30 stat31 stat32 stat33 stat34
## 1 0.7991088 -2.0059092 -0.2461502 0.6482101 -2.87462163 -0.3601543
## 2 1.9758110 -0.3874187 1.3566630 2.6493473 2.28463054 1.8591728
## 3 -0.4460218 1.0279679 1.3998452 -1.0183365 1.41109037 -2.4183984
## 4 1.0571996 2.5588036 -2.9830337 -1.1299983 0.05470414 -1.5566561
## 5 0.8854889 2.2774174 2.6499031 2.3053405 -2.39148426 -1.8272992
## 6 -0.8405267 0.1311945 0.4321289 -2.9622040 -2.55387473 2.6396458
## stat35 stat36 stat37 stat38 stat39 stat40
## 1 2.4286051 -0.5420244 -2.6782637 -2.8874269 -0.8945006 1.1749642
## 2 1.3709245 -1.3714181 1.3901204 1.2273489 -0.8934880 1.0540369
## 3 -0.9805572 2.0571353 0.8845031 2.0574493 1.1222047 1.8528618
## 4 1.0969149 -2.2820673 1.8852408 0.5391517 2.7334342 -0.4372566
## 5 -1.0971669 1.4867796 -2.3738465 -0.3743561 1.4266498 1.2551680
## 6 0.4584349 -2.2696617 -0.9935142 -0.5350499 -0.7874799 2.0009417
## stat41 stat42 stat43 stat44 stat45 stat46
## 1 -1.0474428 -1.3909023 2.54110503 -1.4320793 0.6298335 -2.09296608
## 2 2.5380247 1.6476108 0.44128850 -2.5049477 1.2726039 1.72492969
## 3 1.1477574 0.2288794 0.08891252 2.3044751 -0.7735722 -0.07302936
## 4 -1.3808300 -2.7900956 2.38297582 0.1686397 -2.1591296 1.60828602
## 5 0.2257536 1.9542116 2.66429019 0.8026123 -1.5521187 1.61751962
## 6 -1.3364114 -2.2898803 2.80735397 -0.8413086 1.0057797 -1.50653386
## stat47 stat48 stat49 stat50 stat51 stat52
## 1 -2.8318939 2.1445766 0.5668035 0.1544579 0.6291955 2.2197027
## 2 -0.5804687 -1.3689737 1.4908396 1.2465997 0.8896304 -2.6024318
## 3 0.7918019 1.5712964 1.1038082 -0.2545658 -2.1662638 0.2660159
## 4 -1.8894132 0.5680230 -0.7023218 -0.3972188 0.1578027 2.1770194
## 5 2.1088455 -2.7195437 2.1961412 -0.2615084 1.2109556 0.8260623
## 6 -1.4400891 -0.9421459 -1.7324599 -2.1720727 -2.8129435 0.6958785
## stat53 stat54 stat55 stat56 stat57 stat58
## 1 2.176805 0.5546907 -2.19704103 -0.2884173 1.3232913 -1.32824039
## 2 -2.107441 1.3864788 0.08781975 1.9998228 0.8014438 -0.26979154
## 3 1.234197 2.1337581 1.65231645 -0.4388691 -0.1811156 2.11277962
## 4 2.535406 -2.1387620 0.12856023 -1.9906180 0.9626449 1.65232646
## 5 -2.457080 2.1633499 0.60441124 2.5449364 -1.4978440 2.60542655
## 6 2.003033 -0.5379940 -2.19647264 -1.1954677 -0.5974466 -0.04703835
## stat59 stat60 stat61 stat62 stat63 stat64
## 1 1.24239659 -2.5798278 1.327928 1.68560362 0.6284891 -1.6798652
## 2 0.06379301 0.9465770 1.116928 0.03128772 -2.1944375 0.3382609
## 3 0.93223447 2.4597080 0.465251 -1.71033382 -0.5156728 1.8276784
## 4 -0.29840910 0.7273473 -2.313066 -1.47696018 2.5910559 -1.5127999
## 5 -1.17610002 -1.7948418 -2.669305 0.17813617 2.8956099 2.9411416
## 6 -1.01793981 0.2817057 2.228023 -0.86494124 -0.9747949 -0.1569053
## stat65 stat66 stat67 stat68 stat69 stat70
## 1 -2.9490898 -0.3325469 1.5745990 -2.2978280 1.5451891 -1.345990
## 2 -1.1174885 -1.5728682 -2.9229002 0.2658547 -1.9616533 2.506130
## 3 -0.2231264 -0.4503301 0.7932286 -1.2453773 -2.2309763 2.309761
## 4 -0.3522418 -2.0720532 0.9442933 2.9212906 0.5100371 -2.441108
## 5 -2.1648991 1.2002029 2.8266985 0.7461294 1.6772674 -1.280000
## 6 -2.2295458 1.1446493 0.2024925 -0.2983998 -2.8203752 1.224030
## stat71 stat72 stat73 stat74 stat75 stat76
## 1 1.0260956 2.1071210 2.6625669 -2.8924677 -0.02132523 -2.5058765
## 2 0.3525076 1.6922342 -1.2167022 -1.7271879 2.21176434 1.9329683
## 3 -2.1799035 -2.2645276 0.1415582 0.9887453 1.95592320 0.2951785
## 4 -2.4051409 2.0876484 -0.8632146 0.4011389 -1.16986716 -1.2391174
## 5 1.3538754 -0.8089395 -0.5122626 -2.1696892 1.07344925 2.6696169
## 6 -2.8073371 -1.4450488 0.5481212 -1.4381690 0.80917043 -0.1365944
## stat77 stat78 stat79 stat80 stat81 stat82
## 1 -2.5631845 -2.40331340 0.38416120 -1.2564875 -0.1550840 -1.1762617
## 2 -0.4462085 0.38400793 1.80483031 -0.8387642 0.7624431 0.9936900
## 3 1.6757870 -1.81900752 2.70904708 -0.3201959 2.5754235 1.6346260
## 4 -2.1012006 -2.24691081 1.78056848 1.0323739 1.0762523 2.1343851
## 5 -2.5736733 -1.99958372 -0.05388495 -2.5630073 -2.8783002 -0.5752426
## 6 1.6143972 0.03233746 2.90835762 1.4000487 2.9275615 -2.8503830
## stat83 stat84 stat85 stat86 stat87 stat88
## 1 1.2840565 -2.6794965 1.3956039 -1.5290235 2.221152 2.3794982
## 2 -0.2380048 1.9314318 -1.6747955 -0.3663656 1.582659 -0.5222489
## 3 -0.9150769 -1.5520337 2.4186287 2.7273662 1.306642 0.1320062
## 4 -2.5824408 -2.7775943 0.5085060 0.4689015 2.053348 0.7957955
## 5 -1.0017741 -0.2009138 0.3770109 2.4335201 -1.118058 1.3953410
## 6 2.4891765 2.9931953 -1.4171852 0.3905659 -1.856119 -2.1690490
## stat89 stat90 stat91 stat92 stat93 stat94
## 1 -0.9885110 -0.8873261 -2.7810929 -1.53325891 2.6002395 1.8890998
## 2 0.9982028 -1.2382015 -0.1574496 0.41086048 -0.5412626 -0.2421387
## 3 0.5956759 1.6871066 2.2452753 2.74279594 -1.5860478 2.9393122
## 4 2.0902634 2.1752586 -2.0677712 -2.37861037 1.1653302 0.1500632
## 5 2.9820614 0.8111660 -0.7842287 0.03766387 -1.1681970 2.1217251
## 6 -1.7428021 0.1579032 1.7456742 -0.36858466 -0.1304616 -1.4555819
## stat95 stat96 stat97 stat98 stat99 stat100
## 1 -2.6056035 -0.5814857 2.57652426 -2.3297751 2.6324007 1.445827
## 2 -2.0271583 -0.9126074 2.49582648 0.9745382 1.1339203 -2.549544
## 3 0.3823181 -0.6324139 2.46221566 1.1151560 0.4624891 0.107072
## 4 2.6414623 -0.6630505 2.10394859 1.2627635 0.4861740 1.697012
## 5 1.4642254 2.6485956 -0.07699547 0.6219473 -1.8815142 -2.685463
## 6 1.8937331 -0.4690555 1.04671776 -0.5879866 -0.9766789 2.405940
## stat101 stat102 stat103 stat104 stat105 stat106
## 1 -2.1158021 2.603936 1.7745128 -1.8903574 -1.8558655 1.0122044
## 2 -2.7998588 -2.267895 0.5336456 -0.2859477 -0.5196246 -0.9417582
## 3 0.7969509 -1.744906 -0.7960327 1.9767258 -0.2007264 -0.7872376
## 4 1.7071959 -1.540221 1.6770362 1.5395796 -0.4855365 -1.2894115
## 5 -1.4627420 -1.700983 2.4376490 0.2731541 1.5275587 1.3256483
## 6 2.6888530 1.090155 2.0769854 1.9615480 1.8689761 2.8975825
## stat107 stat108 stat109 stat110 stat111 stat112
## 1 1.954508 -0.3376471 2.503084 0.3099165 2.7209847 -2.3911204
## 2 -2.515160 0.3998704 -1.077093 2.4228268 -0.7759693 0.2513882
## 3 1.888827 1.5819857 -2.066659 -2.0008364 0.6997684 2.6157095
## 4 1.076395 -1.8524148 -2.689204 1.0985872 1.2389493 2.1018629
## 5 2.828866 -1.8590252 -2.424163 1.4391942 -0.6173239 -1.5218846
## 6 -1.419639 0.7888914 1.996463 0.9813507 0.9034198 1.3810679
## stat113 stat114 stat115 stat116 stat117 stat118
## 1 -1.616161 1.0878664 0.9860094 -0.06288462 -1.013501 -1.2212842
## 2 -1.554771 1.8683100 0.4880588 -0.63865489 -1.610217 -1.7713343
## 3 -2.679801 -2.9486952 1.7753417 0.90311784 -1.318836 -0.1429040
## 4 2.459229 -0.5584171 0.4419581 -0.09586351 0.595442 0.2883342
## 5 -2.102200 1.6300170 -2.3498287 1.36771894 -1.912202 -0.2563821
## 6 -1.835037 0.6577786 -2.9928374 2.13540316 -1.437299 -0.9570006
## stat119 stat120 stat121 stat122 stat123 stat124
## 1 2.9222729 1.9151262 1.6686068 2.0061224 1.5723072 0.78819227
## 2 2.1828208 0.8283178 -2.4458632 1.7133740 1.1393738 -0.07182054
## 3 0.9721319 1.2723130 2.8002086 2.7670381 -2.2252586 2.17499113
## 4 -1.9327896 -2.5369370 1.7835028 1.0262097 -1.8790983 -0.43639564
## 5 1.3230809 -2.8145256 -0.9547533 -2.0435417 -0.2758764 -1.85668027
## 6 0.1720700 -1.4568460 1.4115051 -0.9878145 2.3895061 -2.33730745
## stat125 stat126 stat127 stat128 stat129 stat130
## 1 1.588372 1.1620011 -0.2474264 1.650328 2.5147598 0.37283245
## 2 -1.173771 0.8162020 0.3510315 -1.263667 1.7245284 -0.72852904
## 3 -1.503497 -0.5656394 2.8040256 -2.139287 -1.7221642 2.17899609
## 4 1.040967 -2.9039600 0.3103742 1.462339 -1.2940350 -2.95015502
## 5 -2.866184 1.6885070 -2.2525666 -2.628631 1.8581577 2.80127025
## 6 -1.355111 1.5017927 0.4295921 -0.580415 0.9851009 -0.03773117
## stat131 stat132 stat133 stat134 stat135 stat136
## 1 -0.09028241 0.5194538 2.8478346 2.6664724 -2.0206311 1.398415090
## 2 -0.53045595 1.4134049 2.9180586 0.3299096 1.4784122 -1.278896090
## 3 1.35843194 0.2279946 0.3532595 0.6138676 -0.3443284 0.057763811
## 4 -1.92450273 1.2698178 -1.5299660 -2.6083462 1.1665530 -0.187791914
## 5 1.49036849 2.6337729 -2.3206244 0.4978287 -1.7397571 0.001200184
## 6 -0.64642709 -1.9256228 1.7032650 -0.9152725 -0.3188055 2.155395980
## stat137 stat138 stat139 stat140 stat141 stat142
## 1 -1.2794871 0.4064890 -0.4539998 2.6660173 -1.8375313 0.4711883
## 2 -2.7709017 -1.6303773 -1.9025910 0.2572918 0.6612002 1.4764348
## 3 -1.1930757 -0.1051243 -0.5108380 -1.0879666 2.4969513 -0.9477230
## 4 -1.2318919 2.2348571 0.1788580 -1.5851788 -1.2384283 -2.1859181
## 5 1.8685058 2.7229517 -2.9077182 2.6606939 -1.5963592 -2.2213492
## 6 -0.4807318 -1.2117369 -0.9358531 -2.5100758 -2.3803916 -0.7096854
## stat143 stat144 stat145 stat146 stat147 stat148
## 1 1.9466263 2.2689433 -0.3597288 -0.6551386 1.65438592 0.6404466
## 2 1.3156421 2.4459090 -0.3790028 1.4858465 -0.07784461 1.0096149
## 3 0.1959563 2.3062942 1.8459278 2.6848175 -2.70935774 -1.2093409
## 4 1.7633296 -2.8171508 2.0902622 -2.6625464 -1.12600601 -2.1926479
## 5 0.3885758 1.8160636 2.8257299 -1.4526173 1.60679603 2.3807991
## 6 0.7623450 0.2692145 -2.4307463 -2.1244523 -2.67803812 -1.5273387
## stat149 stat150 stat151 stat152 stat153 stat154
## 1 0.1583575 0.4755351 0.3213410 2.0241520 1.5720103 -0.1825875
## 2 -0.4311406 2.9577663 0.6937252 0.1397280 0.3775735 -1.1012636
## 3 -0.8352824 2.5716205 1.7528236 0.4326277 -2.2334397 -2.6265771
## 4 -2.8069143 1.8813509 2.3358023 0.1015632 1.2117474 -1.3714278
## 5 -1.6166265 1.1112266 -1.1998471 2.9316769 -2.1676455 -0.3411089
## 6 -0.2265472 2.7264354 -1.6746094 -2.3376281 -1.7022788 -1.2352397
## stat155 stat156 stat157 stat158 stat159 stat160
## 1 -1.139657 0.07061254 0.5893906 -1.9920996 -2.83714366 2.249398
## 2 -2.041093 0.74047768 2.5415072 -1.2697256 -1.64364433 -2.448922
## 3 -1.219507 -0.55198693 0.4046920 1.2098547 -0.90412390 -1.934093
## 4 2.992191 2.33222485 2.0622969 -0.6714653 2.76836085 -1.431120
## 5 -2.362356 -1.23906672 0.4746319 -0.7849202 0.69399995 2.052411
## 6 -1.604499 1.31051409 -0.5164744 0.6288667 0.07899523 -2.287402
## stat161 stat162 stat163 stat164 stat165 stat166
## 1 1.7182635 -1.2323593 2.7350423 1.0707235 1.1621544 0.9493989
## 2 -0.6247674 2.6740098 2.8211024 1.5561292 -1.1027147 1.0519739
## 3 -0.6230453 -0.7993517 -2.8318374 -1.1148673 1.4261659 0.5294309
## 4 1.7644744 0.1696584 1.2653207 0.6621516 0.9470508 0.1985014
## 5 -1.2070210 0.7243784 0.9736322 2.7426259 -2.6862383 1.6840212
## 6 2.3705316 -2.1667893 -0.2516685 -0.8425958 -1.9099342 -2.8607297
## stat167 stat168 stat169 stat170 stat171 stat172
## 1 0.1146510 2.3872008 1.1180918 -0.95370555 -2.25076509 0.2348182
## 2 1.0760417 -2.0449336 0.9715676 -0.40173489 -0.11953555 -2.3107369
## 3 1.1735898 1.3860190 -2.2894719 0.06350347 0.29191551 -1.6079744
## 4 2.5511832 0.5446648 1.2694012 -0.84571201 0.79789722 0.2623538
## 5 2.2900002 2.6289782 -0.2783571 1.39032829 -0.55532032 1.0499046
## 6 -0.7513983 2.9617066 -2.2119520 -1.71958113 -0.01452018 -0.2751517
## stat173 stat174 stat175 stat176 stat177 stat178
## 1 1.79366076 -1.920206 -0.38841942 0.8530301 1.64532077 -1.1354179
## 2 -0.07484659 1.337846 2.20911694 0.9616837 -2.80810070 -2.1136749
## 3 -1.05521810 -1.483741 0.06148359 2.3066039 -0.34688616 1.1840581
## 4 0.31460321 1.195741 2.97633862 1.1685091 -0.06346265 1.4205489
## 5 -1.39428365 2.458523 0.64836472 -1.0396386 -0.57828104 -0.5006818
## 6 2.31844401 1.239864 -2.06490874 0.7696204 -1.77586019 2.0855925
## stat179 stat180 stat181 stat182 stat183 stat184
## 1 2.0018647 0.1476815 -1.27279520 1.9181504 -0.5297624 -2.9718938
## 2 -2.1351449 2.9012582 -1.09914911 -2.5488517 -2.8377736 1.4073374
## 3 -1.7819908 2.9902627 0.81908613 0.2503852 0.3712984 -2.1714024
## 4 -0.1026974 -2.4763253 -2.52645421 1.3096315 2.1458161 -1.5228094
## 5 -2.2298794 2.4465680 -0.70346898 -1.6997617 2.9178164 -0.3615532
## 6 -1.1168108 1.5552123 -0.01361342 1.7338791 -1.1104763 0.1882416
## stat185 stat186 stat187 stat188 stat189 stat190
## 1 -0.1043832 -1.5047463 2.700351 -2.4780862 -1.9078265 0.9978108
## 2 -2.0310574 -0.5380074 -1.963275 -1.2221278 -2.4290681 -1.9515115
## 3 2.6727278 1.2688179 -1.399018 -2.9612138 2.6456394 2.0073323
## 4 -2.7796295 2.0682354 2.243727 0.4296881 0.1931333 2.2710960
## 5 -0.6231265 2.5833981 2.229041 0.8139584 1.4544131 1.8886451
## 6 2.7204690 -2.4469144 -1.421998 1.7477882 -0.1481806 0.6011560
## stat191 stat192 stat193 stat194 stat195 stat196
## 1 -0.6644351 2.6270833 -1.1094601 -2.4200392 2.870713 -0.6590932
## 2 -0.6483142 1.4519118 -0.1963493 -2.3025322 1.255608 2.1617947
## 3 -1.5457382 -0.2977442 -1.7045015 0.7962404 -1.696063 -1.4771117
## 4 -1.1780495 -2.9747574 -1.1471518 -1.2377013 -1.010672 -2.6055975
## 5 2.8813178 -1.8964081 -1.2653487 -1.7839754 -2.872581 2.3033464
## 6 0.4437973 0.6599325 -1.4029555 -2.3118258 -1.792232 1.3934380
## stat197 stat198 stat199 stat200 stat201 stat202
## 1 -0.83056986 0.9550526 -1.7025776 -2.8263099 -0.7023998 0.2272806
## 2 -1.42178249 -1.2471864 2.5723093 -0.0233496 -1.8975239 1.9472262
## 3 -0.19233958 -0.5161456 0.0279946 -1.2333704 -2.9672263 -2.8666208
## 4 -1.23145902 1.4728470 -0.4562025 -2.2983441 -1.5101184 0.2530525
## 5 1.85018563 -1.8269292 -0.6337969 -2.1473246 0.9909850 1.0950903
## 6 -0.09311061 0.5144456 -2.8178268 -2.7555969 -2.3546004 -1.0558939
## stat203 stat204 stat205 stat206 stat207 stat208
## 1 1.166631220 0.007453276 2.9961641 1.5327307 -2.2293356 -0.9946009
## 2 -0.235396504 2.132749800 0.3707606 1.5604026 -1.0089217 2.1474257
## 3 0.003180946 2.229793310 2.7354040 0.8992231 2.9694967 2.3081024
## 4 -0.474482715 -1.584772230 -2.3224132 -0.9409741 -2.3179255 0.8032548
## 5 2.349412920 -1.276320220 -2.0203719 -1.1733509 1.0371852 -2.5086207
## 6 0.727436960 -0.960191786 -0.8964998 -1.6406623 -0.2330488 1.7993879
## stat209 stat210 stat211 stat212 stat213 stat214
## 1 -2.2182105 -1.4099774 -1.656754 2.6602585 -2.9270992 1.1240714
## 2 -2.8932488 -1.1641679 -2.605423 -1.5650513 2.9523673 2.0266318
## 3 -1.8279589 0.0472350 -2.026734 2.5054367 0.9903042 0.3274105
## 4 -1.0878067 0.1171303 2.645891 -1.6775225 1.3452160 1.4694063
## 5 -0.8158175 0.4060950 0.912256 0.2925677 2.1610141 0.5679936
## 6 -2.2664354 -0.2061083 -1.435174 2.6645632 0.4216259 -0.6419122
## stat215 stat216 stat217
## 1 -2.7510750 -0.5501796 1.2638469
## 2 2.8934650 -2.4099574 -1.2411407
## 3 -1.0947676 1.2852937 1.5411530
## 4 0.6343777 0.1345372 2.9102673
## 5 0.9908702 1.7909757 -2.0902610
## 6 -2.8113887 -1.0624912 0.2765074head(features.highprec)## JobName x1 x2 x3 x4 x5 x6
## 1 Job_00001 2.0734508 4.917267 19.96188 3.520878 7.861051 1.6067589
## 2 Job_00002 2.2682543 4.955773 19.11939 19.763031 6.931355 1.3622041
## 3 Job_00003 1.7424456 2.059819 13.37912 38.829132 6.274053 2.0529845
## 4 Job_00004 0.7873555 2.613983 17.23044 64.402557 5.377652 0.9067419
## 5 Job_00005 2.3342753 4.299076 14.64883 52.537304 6.793368 2.4605792
## 6 Job_00006 1.2365089 2.795370 11.13127 96.819939 6.583971 2.3510606
## x7 x8 x9 x10 x11 x12 x13
## 1 2.979479 8.537228 1.103368 4.6089458 1.050025e-07 7.995825 13.215498
## 2 2.388119 6.561461 0.588572 1.0283282 1.034518e-07 7.486966 22.557224
## 3 2.043592 10.275595 4.834385 4.3872848 1.062312e-07 6.350142 15.049810
## 4 2.395118 13.487331 3.340190 4.5053501 9.471887e-08 9.548698 17.170635
## 5 2.891535 9.362389 1.246039 1.7333300 1.010552e-07 9.596095 5.794567
## 6 1.247838 7.033354 1.852231 0.4839371 1.071662e-07 3.810983 23.863169
## x14 x15 x16 x17 x18 x19 x20
## 1 4.377983 0.2370623 6.075459 3.988347 4.767475 2.698775 1.035893
## 2 2.059315 0.5638121 6.903891 4.152054 6.849232 9.620731 1.915288
## 3 3.260057 2.0603445 8.424065 4.489893 3.493591 4.715386 1.558103
## 4 3.093478 1.8806034 11.189792 2.134271 5.588357 5.107871 1.489588
## 5 3.943076 1.5820830 7.096742 3.563378 7.765610 1.360272 1.240283
## 6 1.280562 1.1733382 7.062051 1.341864 7.748325 5.009365 1.725179
## x21 x22 x23 stat1 stat2 stat3 stat4
## 1 42.36548 1.356213 2.699796 2.3801832 0.1883335 -1.2284011 -0.5999233
## 2 26.63295 4.053961 2.375127 -1.4069480 1.8140973 1.6204884 2.6422672
## 3 20.09693 3.079888 4.488420 -0.7672566 -0.1230289 1.1415752 2.9805934
## 4 32.60415 1.355396 3.402398 0.4371202 -1.9355906 0.9028624 -1.6025400
## 5 44.58361 1.940301 2.249011 2.4492466 -0.6172000 -2.5520642 -2.1485929
## 6 28.75102 2.500499 5.563972 -1.7899084 1.8853619 2.4154840 -2.6022179
## stat5 stat6 stat7 stat8 stat9 stat10
## 1 0.148893163 -0.6622978 -2.4851868 0.3647782 2.5364335 2.92067981
## 2 1.920768980 1.7411555 -1.9599979 -2.0190558 -1.3732762 -0.31642506
## 3 2.422584300 -0.4166040 2.2205689 -2.6741531 0.4844292 2.73379230
## 4 -0.001795933 -0.6946563 -0.3693534 -0.9709467 1.7960306 0.74771154
## 5 -2.311132430 -1.0166832 2.7269876 1.5424492 -1.3156369 -0.09767897
## 6 -1.785491470 -1.8599915 1.4875095 2.0188572 -1.4892503 -1.41103566
## stat11 stat12 stat13 stat14 stat15 stat16
## 1 -2.3228905 -2.480567 -0.6335157 -0.3650149 -0.5322812 0.6029300
## 2 -0.8547903 1.119316 0.7227427 0.2121097 -0.1452281 -2.0361528
## 3 -2.1821580 2.865401 -2.9756081 2.9871745 1.9539525 -1.8857163
## 4 1.3982378 1.856765 -1.0379983 2.3341896 2.3057184 -2.8947697
## 5 0.9567220 2.567549 0.3184886 1.0307668 0.1644241 -0.6613821
## 6 0.5341771 -1.461822 0.4402476 -1.9282095 -0.3680157 1.8188807
## stat17 stat18 stat19 stat20 stat21 stat22
## 1 -1.04516208 2.3544915 2.4049001 0.2633883 -0.9788178 1.7868229
## 2 0.09513074 0.4727738 1.8899702 2.7892542 -1.3919091 -1.7198164
## 3 0.40285346 1.4655282 -1.4952788 2.9162340 -2.3893208 2.8161423
## 4 2.97446084 2.3896182 2.3083484 -1.1894441 -2.1982553 1.3666242
## 5 -0.98465055 0.6900643 1.5894209 -2.1204538 1.7961155 -0.9362189
## 6 -1.45726359 -2.1139548 -0.3964904 1.1764175 -2.9100556 -2.1359294
## stat23 stat24 stat25 stat26 stat27 stat28
## 1 -2.3718851 2.8580718 -0.4719713 -2.817086 -0.9518474 2.88892484
## 2 -2.3293245 1.5577759 -1.9569720 1.554194 -0.5081459 -1.58715141
## 3 -2.5402296 0.1422861 0.3572798 -1.051886 -2.1541717 0.03074004
## 4 -1.9679050 -1.4077642 2.5097435 1.683121 -0.2549745 -2.90384054
## 5 2.0523429 -2.2084844 -1.9280857 -2.116736 1.8180779 -1.42167580
## 6 0.2184991 -0.7599817 2.6880329 -2.903350 -1.0733233 -2.92416644
## stat29 stat30 stat31 stat32 stat33 stat34
## 1 0.7991088 -2.0059092 -0.2461502 0.6482101 -2.87462163 -0.3601543
## 2 1.9758110 -0.3874187 1.3566630 2.6493473 2.28463054 1.8591728
## 3 -0.4460218 1.0279679 1.3998452 -1.0183365 1.41109037 -2.4183984
## 4 1.0571996 2.5588036 -2.9830337 -1.1299983 0.05470414 -1.5566561
## 5 0.8854889 2.2774174 2.6499031 2.3053405 -2.39148426 -1.8272992
## 6 -0.8405267 0.1311945 0.4321289 -2.9622040 -2.55387473 2.6396458
## stat35 stat36 stat37 stat38 stat39 stat40
## 1 2.4286051 -0.5420244 -2.6782637 -2.8874269 -0.8945006 1.1749642
## 2 1.3709245 -1.3714181 1.3901204 1.2273489 -0.8934880 1.0540369
## 3 -0.9805572 2.0571353 0.8845031 2.0574493 1.1222047 1.8528618
## 4 1.0969149 -2.2820673 1.8852408 0.5391517 2.7334342 -0.4372566
## 5 -1.0971669 1.4867796 -2.3738465 -0.3743561 1.4266498 1.2551680
## 6 0.4584349 -2.2696617 -0.9935142 -0.5350499 -0.7874799 2.0009417
## stat41 stat42 stat43 stat44 stat45 stat46
## 1 -1.0474428 -1.3909023 2.54110503 -1.4320793 0.6298335 -2.09296608
## 2 2.5380247 1.6476108 0.44128850 -2.5049477 1.2726039 1.72492969
## 3 1.1477574 0.2288794 0.08891252 2.3044751 -0.7735722 -0.07302936
## 4 -1.3808300 -2.7900956 2.38297582 0.1686397 -2.1591296 1.60828602
## 5 0.2257536 1.9542116 2.66429019 0.8026123 -1.5521187 1.61751962
## 6 -1.3364114 -2.2898803 2.80735397 -0.8413086 1.0057797 -1.50653386
## stat47 stat48 stat49 stat50 stat51 stat52
## 1 -2.8318939 2.1445766 0.5668035 0.1544579 0.6291955 2.2197027
## 2 -0.5804687 -1.3689737 1.4908396 1.2465997 0.8896304 -2.6024318
## 3 0.7918019 1.5712964 1.1038082 -0.2545658 -2.1662638 0.2660159
## 4 -1.8894132 0.5680230 -0.7023218 -0.3972188 0.1578027 2.1770194
## 5 2.1088455 -2.7195437 2.1961412 -0.2615084 1.2109556 0.8260623
## 6 -1.4400891 -0.9421459 -1.7324599 -2.1720727 -2.8129435 0.6958785
## stat53 stat54 stat55 stat56 stat57 stat58
## 1 2.176805 0.5546907 -2.19704103 -0.2884173 1.3232913 -1.32824039
## 2 -2.107441 1.3864788 0.08781975 1.9998228 0.8014438 -0.26979154
## 3 1.234197 2.1337581 1.65231645 -0.4388691 -0.1811156 2.11277962
## 4 2.535406 -2.1387620 0.12856023 -1.9906180 0.9626449 1.65232646
## 5 -2.457080 2.1633499 0.60441124 2.5449364 -1.4978440 2.60542655
## 6 2.003033 -0.5379940 -2.19647264 -1.1954677 -0.5974466 -0.04703835
## stat59 stat60 stat61 stat62 stat63 stat64
## 1 1.24239659 -2.5798278 1.327928 1.68560362 0.6284891 -1.6798652
## 2 0.06379301 0.9465770 1.116928 0.03128772 -2.1944375 0.3382609
## 3 0.93223447 2.4597080 0.465251 -1.71033382 -0.5156728 1.8276784
## 4 -0.29840910 0.7273473 -2.313066 -1.47696018 2.5910559 -1.5127999
## 5 -1.17610002 -1.7948418 -2.669305 0.17813617 2.8956099 2.9411416
## 6 -1.01793981 0.2817057 2.228023 -0.86494124 -0.9747949 -0.1569053
## stat65 stat66 stat67 stat68 stat69 stat70
## 1 -2.9490898 -0.3325469 1.5745990 -2.2978280 1.5451891 -1.345990
## 2 -1.1174885 -1.5728682 -2.9229002 0.2658547 -1.9616533 2.506130
## 3 -0.2231264 -0.4503301 0.7932286 -1.2453773 -2.2309763 2.309761
## 4 -0.3522418 -2.0720532 0.9442933 2.9212906 0.5100371 -2.441108
## 5 -2.1648991 1.2002029 2.8266985 0.7461294 1.6772674 -1.280000
## 6 -2.2295458 1.1446493 0.2024925 -0.2983998 -2.8203752 1.224030
## stat71 stat72 stat73 stat74 stat75 stat76
## 1 1.0260956 2.1071210 2.6625669 -2.8924677 -0.02132523 -2.5058765
## 2 0.3525076 1.6922342 -1.2167022 -1.7271879 2.21176434 1.9329683
## 3 -2.1799035 -2.2645276 0.1415582 0.9887453 1.95592320 0.2951785
## 4 -2.4051409 2.0876484 -0.8632146 0.4011389 -1.16986716 -1.2391174
## 5 1.3538754 -0.8089395 -0.5122626 -2.1696892 1.07344925 2.6696169
## 6 -2.8073371 -1.4450488 0.5481212 -1.4381690 0.80917043 -0.1365944
## stat77 stat78 stat79 stat80 stat81 stat82
## 1 -2.5631845 -2.40331340 0.38416120 -1.2564875 -0.1550840 -1.1762617
## 2 -0.4462085 0.38400793 1.80483031 -0.8387642 0.7624431 0.9936900
## 3 1.6757870 -1.81900752 2.70904708 -0.3201959 2.5754235 1.6346260
## 4 -2.1012006 -2.24691081 1.78056848 1.0323739 1.0762523 2.1343851
## 5 -2.5736733 -1.99958372 -0.05388495 -2.5630073 -2.8783002 -0.5752426
## 6 1.6143972 0.03233746 2.90835762 1.4000487 2.9275615 -2.8503830
## stat83 stat84 stat85 stat86 stat87 stat88
## 1 1.2840565 -2.6794965 1.3956039 -1.5290235 2.221152 2.3794982
## 2 -0.2380048 1.9314318 -1.6747955 -0.3663656 1.582659 -0.5222489
## 3 -0.9150769 -1.5520337 2.4186287 2.7273662 1.306642 0.1320062
## 4 -2.5824408 -2.7775943 0.5085060 0.4689015 2.053348 0.7957955
## 5 -1.0017741 -0.2009138 0.3770109 2.4335201 -1.118058 1.3953410
## 6 2.4891765 2.9931953 -1.4171852 0.3905659 -1.856119 -2.1690490
## stat89 stat90 stat91 stat92 stat93 stat94
## 1 -0.9885110 -0.8873261 -2.7810929 -1.53325891 2.6002395 1.8890998
## 2 0.9982028 -1.2382015 -0.1574496 0.41086048 -0.5412626 -0.2421387
## 3 0.5956759 1.6871066 2.2452753 2.74279594 -1.5860478 2.9393122
## 4 2.0902634 2.1752586 -2.0677712 -2.37861037 1.1653302 0.1500632
## 5 2.9820614 0.8111660 -0.7842287 0.03766387 -1.1681970 2.1217251
## 6 -1.7428021 0.1579032 1.7456742 -0.36858466 -0.1304616 -1.4555819
## stat95 stat96 stat97 stat98 stat99 stat100
## 1 -2.6056035 -0.5814857 2.57652426 -2.3297751 2.6324007 1.445827
## 2 -2.0271583 -0.9126074 2.49582648 0.9745382 1.1339203 -2.549544
## 3 0.3823181 -0.6324139 2.46221566 1.1151560 0.4624891 0.107072
## 4 2.6414623 -0.6630505 2.10394859 1.2627635 0.4861740 1.697012
## 5 1.4642254 2.6485956 -0.07699547 0.6219473 -1.8815142 -2.685463
## 6 1.8937331 -0.4690555 1.04671776 -0.5879866 -0.9766789 2.405940
## stat101 stat102 stat103 stat104 stat105 stat106
## 1 -2.1158021 2.603936 1.7745128 -1.8903574 -1.8558655 1.0122044
## 2 -2.7998588 -2.267895 0.5336456 -0.2859477 -0.5196246 -0.9417582
## 3 0.7969509 -1.744906 -0.7960327 1.9767258 -0.2007264 -0.7872376
## 4 1.7071959 -1.540221 1.6770362 1.5395796 -0.4855365 -1.2894115
## 5 -1.4627420 -1.700983 2.4376490 0.2731541 1.5275587 1.3256483
## 6 2.6888530 1.090155 2.0769854 1.9615480 1.8689761 2.8975825
## stat107 stat108 stat109 stat110 stat111 stat112
## 1 1.954508 -0.3376471 2.503084 0.3099165 2.7209847 -2.3911204
## 2 -2.515160 0.3998704 -1.077093 2.4228268 -0.7759693 0.2513882
## 3 1.888827 1.5819857 -2.066659 -2.0008364 0.6997684 2.6157095
## 4 1.076395 -1.8524148 -2.689204 1.0985872 1.2389493 2.1018629
## 5 2.828866 -1.8590252 -2.424163 1.4391942 -0.6173239 -1.5218846
## 6 -1.419639 0.7888914 1.996463 0.9813507 0.9034198 1.3810679
## stat113 stat114 stat115 stat116 stat117 stat118
## 1 -1.616161 1.0878664 0.9860094 -0.06288462 -1.013501 -1.2212842
## 2 -1.554771 1.8683100 0.4880588 -0.63865489 -1.610217 -1.7713343
## 3 -2.679801 -2.9486952 1.7753417 0.90311784 -1.318836 -0.1429040
## 4 2.459229 -0.5584171 0.4419581 -0.09586351 0.595442 0.2883342
## 5 -2.102200 1.6300170 -2.3498287 1.36771894 -1.912202 -0.2563821
## 6 -1.835037 0.6577786 -2.9928374 2.13540316 -1.437299 -0.9570006
## stat119 stat120 stat121 stat122 stat123 stat124
## 1 2.9222729 1.9151262 1.6686068 2.0061224 1.5723072 0.78819227
## 2 2.1828208 0.8283178 -2.4458632 1.7133740 1.1393738 -0.07182054
## 3 0.9721319 1.2723130 2.8002086 2.7670381 -2.2252586 2.17499113
## 4 -1.9327896 -2.5369370 1.7835028 1.0262097 -1.8790983 -0.43639564
## 5 1.3230809 -2.8145256 -0.9547533 -2.0435417 -0.2758764 -1.85668027
## 6 0.1720700 -1.4568460 1.4115051 -0.9878145 2.3895061 -2.33730745
## stat125 stat126 stat127 stat128 stat129 stat130
## 1 1.588372 1.1620011 -0.2474264 1.650328 2.5147598 0.37283245
## 2 -1.173771 0.8162020 0.3510315 -1.263667 1.7245284 -0.72852904
## 3 -1.503497 -0.5656394 2.8040256 -2.139287 -1.7221642 2.17899609
## 4 1.040967 -2.9039600 0.3103742 1.462339 -1.2940350 -2.95015502
## 5 -2.866184 1.6885070 -2.2525666 -2.628631 1.8581577 2.80127025
## 6 -1.355111 1.5017927 0.4295921 -0.580415 0.9851009 -0.03773117
## stat131 stat132 stat133 stat134 stat135 stat136
## 1 -0.09028241 0.5194538 2.8478346 2.6664724 -2.0206311 1.398415090
## 2 -0.53045595 1.4134049 2.9180586 0.3299096 1.4784122 -1.278896090
## 3 1.35843194 0.2279946 0.3532595 0.6138676 -0.3443284 0.057763811
## 4 -1.92450273 1.2698178 -1.5299660 -2.6083462 1.1665530 -0.187791914
## 5 1.49036849 2.6337729 -2.3206244 0.4978287 -1.7397571 0.001200184
## 6 -0.64642709 -1.9256228 1.7032650 -0.9152725 -0.3188055 2.155395980
## stat137 stat138 stat139 stat140 stat141 stat142
## 1 -1.2794871 0.4064890 -0.4539998 2.6660173 -1.8375313 0.4711883
## 2 -2.7709017 -1.6303773 -1.9025910 0.2572918 0.6612002 1.4764348
## 3 -1.1930757 -0.1051243 -0.5108380 -1.0879666 2.4969513 -0.9477230
## 4 -1.2318919 2.2348571 0.1788580 -1.5851788 -1.2384283 -2.1859181
## 5 1.8685058 2.7229517 -2.9077182 2.6606939 -1.5963592 -2.2213492
## 6 -0.4807318 -1.2117369 -0.9358531 -2.5100758 -2.3803916 -0.7096854
## stat143 stat144 stat145 stat146 stat147 stat148
## 1 1.9466263 2.2689433 -0.3597288 -0.6551386 1.65438592 0.6404466
## 2 1.3156421 2.4459090 -0.3790028 1.4858465 -0.07784461 1.0096149
## 3 0.1959563 2.3062942 1.8459278 2.6848175 -2.70935774 -1.2093409
## 4 1.7633296 -2.8171508 2.0902622 -2.6625464 -1.12600601 -2.1926479
## 5 0.3885758 1.8160636 2.8257299 -1.4526173 1.60679603 2.3807991
## 6 0.7623450 0.2692145 -2.4307463 -2.1244523 -2.67803812 -1.5273387
## stat149 stat150 stat151 stat152 stat153 stat154
## 1 0.1583575 0.4755351 0.3213410 2.0241520 1.5720103 -0.1825875
## 2 -0.4311406 2.9577663 0.6937252 0.1397280 0.3775735 -1.1012636
## 3 -0.8352824 2.5716205 1.7528236 0.4326277 -2.2334397 -2.6265771
## 4 -2.8069143 1.8813509 2.3358023 0.1015632 1.2117474 -1.3714278
## 5 -1.6166265 1.1112266 -1.1998471 2.9316769 -2.1676455 -0.3411089
## 6 -0.2265472 2.7264354 -1.6746094 -2.3376281 -1.7022788 -1.2352397
## stat155 stat156 stat157 stat158 stat159 stat160
## 1 -1.139657 0.07061254 0.5893906 -1.9920996 -2.83714366 2.249398
## 2 -2.041093 0.74047768 2.5415072 -1.2697256 -1.64364433 -2.448922
## 3 -1.219507 -0.55198693 0.4046920 1.2098547 -0.90412390 -1.934093
## 4 2.992191 2.33222485 2.0622969 -0.6714653 2.76836085 -1.431120
## 5 -2.362356 -1.23906672 0.4746319 -0.7849202 0.69399995 2.052411
## 6 -1.604499 1.31051409 -0.5164744 0.6288667 0.07899523 -2.287402
## stat161 stat162 stat163 stat164 stat165 stat166
## 1 1.7182635 -1.2323593 2.7350423 1.0707235 1.1621544 0.9493989
## 2 -0.6247674 2.6740098 2.8211024 1.5561292 -1.1027147 1.0519739
## 3 -0.6230453 -0.7993517 -2.8318374 -1.1148673 1.4261659 0.5294309
## 4 1.7644744 0.1696584 1.2653207 0.6621516 0.9470508 0.1985014
## 5 -1.2070210 0.7243784 0.9736322 2.7426259 -2.6862383 1.6840212
## 6 2.3705316 -2.1667893 -0.2516685 -0.8425958 -1.9099342 -2.8607297
## stat167 stat168 stat169 stat170 stat171 stat172
## 1 0.1146510 2.3872008 1.1180918 -0.95370555 -2.25076509 0.2348182
## 2 1.0760417 -2.0449336 0.9715676 -0.40173489 -0.11953555 -2.3107369
## 3 1.1735898 1.3860190 -2.2894719 0.06350347 0.29191551 -1.6079744
## 4 2.5511832 0.5446648 1.2694012 -0.84571201 0.79789722 0.2623538
## 5 2.2900002 2.6289782 -0.2783571 1.39032829 -0.55532032 1.0499046
## 6 -0.7513983 2.9617066 -2.2119520 -1.71958113 -0.01452018 -0.2751517
## stat173 stat174 stat175 stat176 stat177 stat178
## 1 1.79366076 -1.920206 -0.38841942 0.8530301 1.64532077 -1.1354179
## 2 -0.07484659 1.337846 2.20911694 0.9616837 -2.80810070 -2.1136749
## 3 -1.05521810 -1.483741 0.06148359 2.3066039 -0.34688616 1.1840581
## 4 0.31460321 1.195741 2.97633862 1.1685091 -0.06346265 1.4205489
## 5 -1.39428365 2.458523 0.64836472 -1.0396386 -0.57828104 -0.5006818
## 6 2.31844401 1.239864 -2.06490874 0.7696204 -1.77586019 2.0855925
## stat179 stat180 stat181 stat182 stat183 stat184
## 1 2.0018647 0.1476815 -1.27279520 1.9181504 -0.5297624 -2.9718938
## 2 -2.1351449 2.9012582 -1.09914911 -2.5488517 -2.8377736 1.4073374
## 3 -1.7819908 2.9902627 0.81908613 0.2503852 0.3712984 -2.1714024
## 4 -0.1026974 -2.4763253 -2.52645421 1.3096315 2.1458161 -1.5228094
## 5 -2.2298794 2.4465680 -0.70346898 -1.6997617 2.9178164 -0.3615532
## 6 -1.1168108 1.5552123 -0.01361342 1.7338791 -1.1104763 0.1882416
## stat185 stat186 stat187 stat188 stat189 stat190
## 1 -0.1043832 -1.5047463 2.700351 -2.4780862 -1.9078265 0.9978108
## 2 -2.0310574 -0.5380074 -1.963275 -1.2221278 -2.4290681 -1.9515115
## 3 2.6727278 1.2688179 -1.399018 -2.9612138 2.6456394 2.0073323
## 4 -2.7796295 2.0682354 2.243727 0.4296881 0.1931333 2.2710960
## 5 -0.6231265 2.5833981 2.229041 0.8139584 1.4544131 1.8886451
## 6 2.7204690 -2.4469144 -1.421998 1.7477882 -0.1481806 0.6011560
## stat191 stat192 stat193 stat194 stat195 stat196
## 1 -0.6644351 2.6270833 -1.1094601 -2.4200392 2.870713 -0.6590932
## 2 -0.6483142 1.4519118 -0.1963493 -2.3025322 1.255608 2.1617947
## 3 -1.5457382 -0.2977442 -1.7045015 0.7962404 -1.696063 -1.4771117
## 4 -1.1780495 -2.9747574 -1.1471518 -1.2377013 -1.010672 -2.6055975
## 5 2.8813178 -1.8964081 -1.2653487 -1.7839754 -2.872581 2.3033464
## 6 0.4437973 0.6599325 -1.4029555 -2.3118258 -1.792232 1.3934380
## stat197 stat198 stat199 stat200 stat201 stat202
## 1 -0.83056986 0.9550526 -1.7025776 -2.8263099 -0.7023998 0.2272806
## 2 -1.42178249 -1.2471864 2.5723093 -0.0233496 -1.8975239 1.9472262
## 3 -0.19233958 -0.5161456 0.0279946 -1.2333704 -2.9672263 -2.8666208
## 4 -1.23145902 1.4728470 -0.4562025 -2.2983441 -1.5101184 0.2530525
## 5 1.85018563 -1.8269292 -0.6337969 -2.1473246 0.9909850 1.0950903
## 6 -0.09311061 0.5144456 -2.8178268 -2.7555969 -2.3546004 -1.0558939
## stat203 stat204 stat205 stat206 stat207 stat208
## 1 1.166631220 0.007453276 2.9961641 1.5327307 -2.2293356 -0.9946009
## 2 -0.235396504 2.132749800 0.3707606 1.5604026 -1.0089217 2.1474257
## 3 0.003180946 2.229793310 2.7354040 0.8992231 2.9694967 2.3081024
## 4 -0.474482715 -1.584772230 -2.3224132 -0.9409741 -2.3179255 0.8032548
## 5 2.349412920 -1.276320220 -2.0203719 -1.1733509 1.0371852 -2.5086207
## 6 0.727436960 -0.960191786 -0.8964998 -1.6406623 -0.2330488 1.7993879
## stat209 stat210 stat211 stat212 stat213 stat214
## 1 -2.2182105 -1.4099774 -1.656754 2.6602585 -2.9270992 1.1240714
## 2 -2.8932488 -1.1641679 -2.605423 -1.5650513 2.9523673 2.0266318
## 3 -1.8279589 0.0472350 -2.026734 2.5054367 0.9903042 0.3274105
## 4 -1.0878067 0.1171303 2.645891 -1.6775225 1.3452160 1.4694063
## 5 -0.8158175 0.4060950 0.912256 0.2925677 2.1610141 0.5679936
## 6 -2.2664354 -0.2061083 -1.435174 2.6645632 0.4216259 -0.6419122
## stat215 stat216 stat217
## 1 -2.7510750 -0.5501796 1.2638469
## 2 2.8934650 -2.4099574 -1.2411407
## 3 -1.0947676 1.2852937 1.5411530
## 4 0.6343777 0.1345372 2.9102673
## 5 0.9908702 1.7909757 -2.0902610
## 6 -2.8113887 -1.0624912 0.2765074features = features.highprec
#str(features) Checking correlations to evaluate removal of redundant features
corr.matrix = round(cor(features[sapply(features, is.numeric)]),2)
# filter out only highly correlated variables
threshold = 0.6
corr.matrix.tmp = corr.matrix
diag(corr.matrix.tmp) = 0
high.corr = apply(abs(corr.matrix.tmp) >= threshold, 1, any)
high.corr.matrix = corr.matrix.tmp[high.corr, high.corr]
DT::datatable(corr.matrix)DT::datatable(high.corr.matrix)Feature Names
feature.names = colnames(features)
drops <- c('JobName')
feature.names = feature.names[!(feature.names %in% drops)]
#str(feature.names)Read and Clean Labels
labels = read.csv("../../Data/labels.csv")
#str(labels)
labels = labels[,c("JobName", output.var)]
summary(labels)## JobName y3
## Job_00001: 1 Min. : 95.91
## Job_00002: 1 1st Qu.:118.21
## Job_00003: 1 Median :123.99
## Job_00004: 1 Mean :125.36
## Job_00005: 1 3rd Qu.:131.06
## Job_00006: 1 Max. :193.73
## (Other) :9994 NA's :2497Merge Datasets
data <- merge(features, labels, by = 'JobName')
drops <- c('JobName')
data = data[,(!colnames(data) %in% drops)]
#str(data)Transformations
if (transform.abs == TRUE){
data[,label.names] = 10^(data[,label.names]/20)
#data = filter(data, y3 < 1E7)
}
if (log.pred == TRUE){
data[label.names] = log(data[alt.scale.label.name],10)
drops = c(alt.scale.label.name)
data = data[!(names(data) %in% drops)]
}
t = NULL # initializw to NULL for other cases
if (norm.pred){
t = bestNormalize::bestNormalize(data[[alt.scale.label.name]])
data[label.names] = predict(t)
drops = c(alt.scale.label.name)
data = data[!(names(data) %in% drops)]
}## Warning in orderNorm(standardize = TRUE, warn = TRUE, x = c(121.2556129, : Ties in data, Normal distribution not guaranteed#str(data)Remove NA Cases
data = data[complete.cases(data),]Exploratory Data Analysis
Check correlation of Label with Featires
if (eda == TRUE){
corr.to.label =round(cor(dplyr::select(data,-one_of(label.names)),dplyr::select_at(data,label.names)),4)
DT::datatable(corr.to.label)
}Multicollinearity - VIF
if (eda == TRUE){
vifDF = usdm::vif(select_at(data,feature.names)) %>% arrange(desc(VIF))
head(vifDF,10)
}Scatterplots
panel.hist <- function(x, ...)
{
usr <- par("usr"); on.exit(par(usr))
par(usr = c(usr[1:2], 0, 1.5) )
h <- hist(x, plot = FALSE)
breaks <- h$breaks; nB <- length(breaks)
y <- h$counts; y <- y/max(y)
rect(breaks[-nB], 0, breaks[-1], y, col = "cyan", ...)
}if (eda == TRUE){
histogram(data[ ,label.names])
#hist(data[complete.cases(data),alt.scale.label.name])
}# https://stackoverflow.com/questions/24648729/plot-one-numeric-variable-against-n-numeric-variables-in-n-plots
ind.pairs.plot <- function(data, xvars=NULL, yvar)
{
df <- data
if (is.null(xvars)) {
xvars = names(data[which(names(data)!=yvar)])
}
#choose a format to display charts
ncharts <- length(xvars)
for(i in 1:ncharts){
plot(df[,xvars[i]],df[,yvar], xlab = xvars[i], ylab = yvar)
}
}
if (eda == TRUE){
ind.pairs.plot(data, feature.names, label.names)
}
#
# pl <- ggplot(data, aes(x=x18, y = y3))
# pl2 <- pl + geom_point(aes(alpha = 0.1)) # default color gradient based on 'hp'
# print(pl2)Feature Engineering
if(eda ==FALSE){
# x18 may need transformations
plot(data[,'x18'], data[,label.names], main = "Original Scatter Plot vs. x18", ylab = label.names, xlab = 'x18')
plot(sqrt(data[,'x18']), data[,label.names], main = "Original Scatter Plot vs. sqrt(x18)", ylab = label.names, xlab = 'sqrt(x18)')
# transforming x18
data$sqrt.x18 = sqrt(data$x18)
data = dplyr::select(data,-one_of('x18'))
# what about x7, x9?
# x11 looks like data is at discrete points after a while. Will this be a problem?
}
Modeling
Train Test Split
data = data[sample(nrow(data)),] # randomly shuffle data
split = sample.split(data[,label.names], SplitRatio = 0.8)
data.train = subset(data, split == TRUE)
data.test = subset(data, split == FALSE)Common Functions
plot.diagnostics <- function(model, train) {
plot(model)
residuals = resid(model) # Plotted above in plot(lm.out)
r.standard = rstandard(model)
r.student = rstudent(model)
plot(predict(model,train),r.student,
ylab="Student Residuals", xlab="Predicted Values",
main="Student Residual Plot")
abline(0, 0)
plot(predict(model, train),r.standard,
ylab="Standard Residuals", xlab="Predicted Values",
main="Standard Residual Plot")
abline(0, 0)
abline(2, 0)
abline(-2, 0)
# Histogram
hist(r.student, freq=FALSE, main="Distribution of Studentized Residuals",
xlab="Studentized Residuals", ylab="Density", ylim=c(0,0.5))
# Create range of x-values for normal curve
xfit <- seq(min(r.student)-1, max(r.student)+1, length=40)
# Generate values from the normal distribution at the specified values
yfit <- (dnorm(xfit))
# Add the normal curve
lines(xfit, yfit, ylim=c(0,0.5))
# http://www.stat.columbia.edu/~martin/W2024/R7.pdf
# Influential plots
inf.meas = influence.measures(model)
# print (summary(inf.meas)) # too much data
# Leverage plot
lev = hat(model.matrix(model))
plot(lev, ylab = 'Leverage - check')
# Cook's Distance
cd = cooks.distance(model)
plot(cd,ylab="Cooks distances")
abline(4/nrow(train),0)
abline(1,0)
print (paste("Number of data points that have Cook's D > 4/n: ", length(cd[cd > 4/nrow(train)]), sep = ""))
print (paste("Number of data points that have Cook's D > 1: ", length(cd[cd > 1]), sep = ""))
return(cd)
}
train.caret.glmselect = function(formula, data, method
,subopt = NULL, feature.names
, train.control = NULL, tune.grid = NULL, pre.proc = NULL){
if(is.null(train.control)){
train.control <- trainControl(method = "cv"
,number = 10
,search = "grid"
,verboseIter = TRUE
,allowParallel = TRUE
)
}
if(is.null(tune.grid)){
if (method == 'leapForward' | method == 'leapBackward' | method == 'leapSeq'){
tune.grid = data.frame(nvmax = 1:length(feature.names))
}
if (method == 'glmnet' && subopt == 'LASSO'){
# Will only show 1 Lambda value during training, but that is OK
# https://stackoverflow.com/questions/47526544/why-need-to-tune-lambda-with-carettrain-method-glmnet-and-cv-glmnet
# Another option for LASSO is this: https://github.com/topepo/caret/blob/master/RegressionTests/Code/lasso.R
lambda = 10^seq(-2,0, length =100)
alpha = c(1)
tune.grid = expand.grid(alpha = alpha,lambda = lambda)
}
if (method == 'lars'){
# https://github.com/topepo/caret/blob/master/RegressionTests/Code/lars.R
fraction = seq(0, 1, length = 100)
tune.grid = expand.grid(fraction = fraction)
pre.proc = c("center", "scale")
}
}
# http://sshaikh.org/2015/05/06/parallelize-machine-learning-in-r-with-multi-core-cpus/
cl <- makeCluster(detectCores()*0.75) # use 75% of cores only, leave rest for other tasks
registerDoParallel(cl)
set.seed(1)
# note that the seed has to actually be set just before this function is called
# settign is above just not ensure reproducibility for some reason
model.caret <- caret::train(formula
, data = data
, method = method
, tuneGrid = tune.grid
, trControl = train.control
, preProc = pre.proc
)
stopCluster(cl)
registerDoSEQ() # register sequential engine in case you are not using this function anymore
if (method == 'leapForward' | method == 'leapBackward' | method == 'leapSeq'){
print(model.caret$results) # all model results
print(model.caret$bestTune) # best model
model = model.caret$finalModel
# Metrics Plot
dataPlot = model.caret$results %>%
gather(key='metric',value='value',-nvmax) %>%
dplyr::filter(metric %in% c('MAE','RMSE','Rsquared'))
metricsPlot = ggplot(data=dataPlot,aes(x=nvmax,y=value) ) +
geom_line(color='lightblue4') +
geom_point(color='blue',alpha=0.7,size=.9) +
facet_wrap(~metric,ncol=4,scales='free_y')+
theme_light()
plot(metricsPlot)
# Residuals Plot
# leap function does not support studentized residuals
dataPlot=data.frame(pred=predict(model.caret,data),res=resid(model.caret))
residPlot = ggplot(dataPlot,aes(x=pred,y=res)) +
geom_point(color='light blue',alpha=0.7) +
geom_smooth()+
theme_light()
plot(residPlot)
residHistogram = ggplot(dataPlot,aes(x=res)) +
geom_histogram(aes(y=..density..),fill='light blue',alpha=1) +
geom_density(color='lightblue4') +
theme_light()
plot(residHistogram)
# Provides the coefficients of the best model
id = rownames(model.caret$bestTune)
message("Coefficients of final model:")
print (coef(model, id = id))
return(list(model = model,id = id, residPlot = residPlot, residHistogram=residHistogram))
}
if (method == 'glmnet' && subopt == 'LASSO'){
print(model.caret)
print(plot(model.caret))
print(model.caret$bestTune)
print(model.caret$results)
# Metrics Plot
dataPlot = model.caret$results %>%
gather(key='metric',value='value',-lambda) %>%
dplyr::filter(metric %in% c('MAE','RMSE','Rsquared'))
metricsPlot = ggplot(data=dataPlot,aes(x=lambda,y=value) ) +
geom_line(color='lightblue4') +
geom_point(color='blue',alpha=0.7,size=.9) +
facet_wrap(~metric,ncol=4,scales='free_y')+
theme_light()
plot(metricsPlot)
# Residuals Plot
dataPlot=data.frame(pred=predict(model.caret,data),res=resid(model.caret))
residPlot = ggplot(dataPlot,aes(x=pred,y=res)) +
geom_point(color='light blue',alpha=0.7) +
geom_smooth()+
theme_light()
plot(residPlot)
residHistogram = ggplot(dataPlot,aes(x=res)) +
geom_histogram(aes(y=..density..),fill='light blue',alpha=1) +
geom_density(color='lightblue4') +
theme_light()
plot(residHistogram)
id = NULL # not really needed but added for consistency
return(list(model = model.caret,id = id, residPlot = residPlot, metricsPlot=metricsPlot ))
}
if (method == 'lars'){
print(model.caret)
print(plot(model.caret))
print(model.caret$bestTune)
# Metrics Plot
dataPlot = model.caret$results %>%
gather(key='metric',value='value',-fraction) %>%
dplyr::filter(metric %in% c('MAE','RMSE','Rsquared'))
metricsPlot = ggplot(data=dataPlot,aes(x=fraction,y=value) ) +
geom_line(color='lightblue4') +
geom_point(color='blue',alpha=0.7,size=.9) +
facet_wrap(~metric,ncol=4,scales='free_y')+
theme_light()
plot(metricsPlot)
# Residuals Plot
dataPlot=data.frame(pred=predict(model.caret,data),res=resid(model.caret))
residPlot = ggplot(dataPlot,aes(x=pred,y=res)) +
geom_point(color='light blue',alpha=0.7) +
geom_smooth()+
theme_light()
plot(residPlot)
residHistogram = ggplot(dataPlot,aes(x=res)) +
geom_histogram(aes(y=..density..),fill='light blue',alpha=1) +
geom_density(color='lightblue4') +
theme_light()
plot(residHistogram)
id = NULL # not really needed but added for consistency
return(list(model = model.caret,id = id, residPlot = residPlot, residHistogram=residHistogram))
}
}
# https://stackoverflow.com/questions/48265743/linear-model-subset-selection-goodness-of-fit-with-k-fold-cross-validation
# changed slightly since call[[2]] was just returning "formula" without actually returnign the value in formula
predict.regsubsets <- function(object, newdata, id, formula, ...) {
#form <- as.formula(object$call[[2]])
mat <- model.matrix(formula, newdata) # adds intercept and expands any interaction terms
coefi <- coef(object, id = id)
xvars <- names(coefi)
return(mat[,xvars]%*%coefi)
}
test.model = function(model, test, level=0.95
,draw.limits = FALSE, good = 0.1, ok = 0.15
,method = NULL, subopt = NULL
,id = NULL, formula, feature.names, label.names
,transformation = NULL){
## if using caret for glm select equivalent functionality,
## need to pass formula (full is ok as it will select subset of variables from there)
if (is.null(method)){
pred = predict(model, newdata=test, interval="confidence", level = level)
}
if (method == 'leapForward' | method == 'leapBackward' | method == 'leapSeq'){
pred = predict.regsubsets(model, newdata = test, id = id, formula = formula)
}
if (method == 'glmnet' && subopt == 'LASSO'){
xtest = as.matrix(test[,feature.names])
pred=as.data.frame(predict(model, xtest))
}
if (method == 'lars'){
pred=as.data.frame(predict(model, newdata = test))
}
# Summary of predicted values
print ("Summary of predicted values: ")
print(summary(pred[,1]))
test.mse = mean((test[,label.names]-pred[,1])^2)
print (paste(method, subopt, "Test MSE:", test.mse, sep=" "))
if(log.pred == TRUE || norm.pred == TRUE){
# plot transformewd comparison first
plot(test[,label.names],pred[,1],xlab = "Actual (Transformed)", ylab = "Predicted (Transformed)")
}
if (log.pred == FALSE && norm.pred == FALSE){
x = test[,label.names]
y = pred[,1]
}
if (log.pred == TRUE){
x = 10^test[,label.names]
y = 10^pred[,1]
}
if (norm.pred == TRUE){
x = predict(transformation, test[,label.names], inverse = TRUE)
y = predict(transformation, pred[,1], inverse = TRUE)
}
plot(x, y, xlab = "Actual", ylab = "Predicted")
abline(0,(1+good),col='green', lwd = 3)
abline(0,(1-good),col='green', lwd = 3)
abline(0,(1+ok),col='blue', lwd = 3)
abline(0,(1-ok),col='blue', lwd = 3)
}Setup Formulae
n <- names(data.train)
formula <- as.formula(paste(paste(n[n %in% label.names], collapse = " + ")," ~", paste(n[!n %in% label.names], collapse = " + ")))
# ind.interact = c("x4","x7","x8", "x9", "x10", "x11", "x14", "x16", "x17", "x21", "sqrt.x18")
# ind.nointeract = c("stat13", "stat14", "stat24", "stat60", "stat98", "stat110", "stat144", "stat149")
#
# interact = paste(ind.interact, collapse = " + ")
# nointeract = paste(ind.nointeract, collapse = " + ")
#
# # ^2 is 2 way interaction, ^3 is 3 way interaction
# formula <- as.formula(paste(paste(n[n %in% label.names], collapse = " + "), "~ (", interact, " )^2 ", " + ", nointeract ))
#
# # # * is all way interaction
# # formula <- as.formula(paste(paste(n[n %in% label.names], collapse = " + "), "~ (", interact, " ) ", " + ", nointeract ))
grand.mean.formula = as.formula(paste(paste(n[n %in% label.names], collapse = " + ")," ~ 1"))
print(formula)## norm.y3 ~ x1 + x2 + x3 + x4 + x5 + x6 + x7 + x8 + x9 + x10 +
## x11 + x12 + x13 + x14 + x15 + x16 + x17 + x19 + x20 + x21 +
## x22 + x23 + stat1 + stat2 + stat3 + stat4 + stat5 + stat6 +
## stat7 + stat8 + stat9 + stat10 + stat11 + stat12 + stat13 +
## stat14 + stat15 + stat16 + stat17 + stat18 + stat19 + stat20 +
## stat21 + stat22 + stat23 + stat24 + stat25 + stat26 + stat27 +
## stat28 + stat29 + stat30 + stat31 + stat32 + stat33 + stat34 +
## stat35 + stat36 + stat37 + stat38 + stat39 + stat40 + stat41 +
## stat42 + stat43 + stat44 + stat45 + stat46 + stat47 + stat48 +
## stat49 + stat50 + stat51 + stat52 + stat53 + stat54 + stat55 +
## stat56 + stat57 + stat58 + stat59 + stat60 + stat61 + stat62 +
## stat63 + stat64 + stat65 + stat66 + stat67 + stat68 + stat69 +
## stat70 + stat71 + stat72 + stat73 + stat74 + stat75 + stat76 +
## stat77 + stat78 + stat79 + stat80 + stat81 + stat82 + stat83 +
## stat84 + stat85 + stat86 + stat87 + stat88 + stat89 + stat90 +
## stat91 + stat92 + stat93 + stat94 + stat95 + stat96 + stat97 +
## stat98 + stat99 + stat100 + stat101 + stat102 + stat103 +
## stat104 + stat105 + stat106 + stat107 + stat108 + stat109 +
## stat110 + stat111 + stat112 + stat113 + stat114 + stat115 +
## stat116 + stat117 + stat118 + stat119 + stat120 + stat121 +
## stat122 + stat123 + stat124 + stat125 + stat126 + stat127 +
## stat128 + stat129 + stat130 + stat131 + stat132 + stat133 +
## stat134 + stat135 + stat136 + stat137 + stat138 + stat139 +
## stat140 + stat141 + stat142 + stat143 + stat144 + stat145 +
## stat146 + stat147 + stat148 + stat149 + stat150 + stat151 +
## stat152 + stat153 + stat154 + stat155 + stat156 + stat157 +
## stat158 + stat159 + stat160 + stat161 + stat162 + stat163 +
## stat164 + stat165 + stat166 + stat167 + stat168 + stat169 +
## stat170 + stat171 + stat172 + stat173 + stat174 + stat175 +
## stat176 + stat177 + stat178 + stat179 + stat180 + stat181 +
## stat182 + stat183 + stat184 + stat185 + stat186 + stat187 +
## stat188 + stat189 + stat190 + stat191 + stat192 + stat193 +
## stat194 + stat195 + stat196 + stat197 + stat198 + stat199 +
## stat200 + stat201 + stat202 + stat203 + stat204 + stat205 +
## stat206 + stat207 + stat208 + stat209 + stat210 + stat211 +
## stat212 + stat213 + stat214 + stat215 + stat216 + stat217 +
## sqrt.x18print(grand.mean.formula)## norm.y3 ~ 1# Update feature.names because we may have transformed some features
feature.names = n[!n %in% label.names]Full Model
model.full = lm(formula , data.train)
summary(model.full)##
## Call:
## lm(formula = formula, data = data.train)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.6319 -0.5798 -0.0821 0.5194 3.9322
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -3.835e+00 2.480e-01 -15.460 < 2e-16 ***
## x1 -7.233e-03 1.702e-02 -0.425 0.670932
## x2 1.029e-02 1.085e-02 0.948 0.343192
## x3 1.859e-03 2.959e-03 0.628 0.529782
## x4 -1.265e-03 2.327e-04 -5.435 5.69e-08 ***
## x5 6.741e-03 7.662e-03 0.880 0.378987
## x6 -2.331e-03 1.551e-02 -0.150 0.880579
## x7 3.414e-01 1.659e-02 20.579 < 2e-16 ***
## x8 9.206e-03 3.848e-03 2.392 0.016775 *
## x9 9.742e-02 8.586e-03 11.347 < 2e-16 ***
## x10 3.911e-02 7.986e-03 4.897 9.98e-07 ***
## x11 5.334e+06 1.922e+06 2.775 0.005531 **
## x12 -4.101e-03 4.869e-03 -0.842 0.399655
## x13 3.982e-03 1.935e-03 2.058 0.039670 *
## x14 -8.619e-03 8.376e-03 -1.029 0.303522
## x15 1.072e-03 8.005e-03 0.134 0.893451
## x16 2.619e-02 5.536e-03 4.730 2.29e-06 ***
## x17 3.261e-02 8.476e-03 3.847 0.000121 ***
## x19 6.184e-03 4.276e-03 1.446 0.148154
## x20 -1.092e-02 2.985e-02 -0.366 0.714618
## x21 4.758e-03 1.099e-03 4.328 1.53e-05 ***
## x22 -1.411e-02 8.921e-03 -1.582 0.113799
## x23 -3.187e-04 8.511e-03 -0.037 0.970127
## stat1 -3.136e-03 6.432e-03 -0.488 0.625829
## stat2 5.801e-03 6.372e-03 0.910 0.362640
## stat3 1.181e-02 6.468e-03 1.826 0.067960 .
## stat4 -1.393e-02 6.444e-03 -2.162 0.030674 *
## stat5 -6.455e-03 6.471e-03 -0.998 0.318558
## stat6 -6.446e-03 6.444e-03 -1.000 0.317206
## stat7 1.590e-03 6.441e-03 0.247 0.805091
## stat8 -3.295e-03 6.459e-03 -0.510 0.610003
## stat9 -1.013e-04 6.422e-03 -0.016 0.987408
## stat10 -7.420e-03 6.412e-03 -1.157 0.247266
## stat11 -1.111e-02 6.499e-03 -1.709 0.087434 .
## stat12 2.252e-03 6.404e-03 0.352 0.725165
## stat13 -7.266e-03 6.437e-03 -1.129 0.259086
## stat14 -2.467e-02 6.403e-03 -3.854 0.000118 ***
## stat15 -7.483e-03 6.391e-03 -1.171 0.241651
## stat16 3.061e-03 6.419e-03 0.477 0.633498
## stat17 -3.934e-03 6.367e-03 -0.618 0.536730
## stat18 -6.051e-03 6.373e-03 -0.949 0.342452
## stat19 5.262e-03 6.412e-03 0.821 0.411925
## stat20 -9.923e-03 6.431e-03 -1.543 0.122879
## stat21 -1.094e-03 6.479e-03 -0.169 0.865931
## stat22 -7.535e-03 6.463e-03 -1.166 0.243706
## stat23 1.464e-02 6.419e-03 2.281 0.022591 *
## stat24 -1.080e-02 6.446e-03 -1.675 0.094020 .
## stat25 -1.018e-02 6.433e-03 -1.583 0.113469
## stat26 -7.021e-03 6.445e-03 -1.089 0.276060
## stat27 2.518e-03 6.414e-03 0.393 0.694657
## stat28 2.452e-03 6.438e-03 0.381 0.703273
## stat29 6.505e-03 6.454e-03 1.008 0.313538
## stat30 5.059e-03 6.521e-03 0.776 0.437895
## stat31 -5.189e-03 6.472e-03 -0.802 0.422725
## stat32 1.982e-03 6.499e-03 0.305 0.760363
## stat33 -1.368e-02 6.438e-03 -2.124 0.033685 *
## stat34 5.679e-03 6.445e-03 0.881 0.378276
## stat35 -7.725e-03 6.405e-03 -1.206 0.227869
## stat36 5.438e-03 6.372e-03 0.853 0.393445
## stat37 -1.131e-02 6.512e-03 -1.736 0.082544 .
## stat38 8.730e-03 6.427e-03 1.358 0.174372
## stat39 -6.666e-03 6.387e-03 -1.044 0.296662
## stat40 1.082e-03 6.429e-03 0.168 0.866414
## stat41 -1.732e-02 6.393e-03 -2.710 0.006756 **
## stat42 -8.632e-03 6.417e-03 -1.345 0.178633
## stat43 -1.146e-02 6.443e-03 -1.779 0.075360 .
## stat44 8.450e-03 6.386e-03 1.323 0.185826
## stat45 -5.952e-03 6.431e-03 -0.926 0.354682
## stat46 9.941e-03 6.443e-03 1.543 0.122903
## stat47 4.048e-03 6.490e-03 0.624 0.532810
## stat48 4.397e-03 6.423e-03 0.685 0.493629
## stat49 4.762e-03 6.383e-03 0.746 0.455678
## stat50 5.713e-03 6.389e-03 0.894 0.371272
## stat51 6.229e-03 6.422e-03 0.970 0.332110
## stat52 -3.701e-03 6.432e-03 -0.575 0.565047
## stat53 -2.280e-03 6.485e-03 -0.352 0.725203
## stat54 -1.029e-02 6.448e-03 -1.596 0.110625
## stat55 8.917e-03 6.355e-03 1.403 0.160609
## stat56 -1.568e-03 6.447e-03 -0.243 0.807870
## stat57 3.190e-03 6.375e-03 0.500 0.616768
## stat58 -1.119e-03 6.398e-03 -0.175 0.861183
## stat59 7.338e-04 6.438e-03 0.114 0.909257
## stat60 1.458e-02 6.432e-03 2.267 0.023438 *
## stat61 -3.006e-03 6.460e-03 -0.465 0.641703
## stat62 -6.957e-03 6.437e-03 -1.081 0.279865
## stat63 4.627e-03 6.423e-03 0.720 0.471351
## stat64 6.275e-05 6.380e-03 0.010 0.992153
## stat65 -2.031e-03 6.433e-03 -0.316 0.752229
## stat66 7.616e-03 6.507e-03 1.170 0.241859
## stat67 5.753e-03 6.483e-03 0.887 0.374937
## stat68 -8.286e-04 6.454e-03 -0.128 0.897853
## stat69 -3.988e-04 6.443e-03 -0.062 0.950650
## stat70 1.363e-03 6.414e-03 0.212 0.831760
## stat71 3.471e-03 6.410e-03 0.541 0.588187
## stat72 -1.285e-03 6.434e-03 -0.200 0.841653
## stat73 1.195e-02 6.447e-03 1.853 0.063898 .
## stat74 -4.099e-03 6.472e-03 -0.633 0.526530
## stat75 -1.802e-03 6.477e-03 -0.278 0.780825
## stat76 6.068e-03 6.423e-03 0.945 0.344880
## stat77 -4.568e-03 6.447e-03 -0.709 0.478653
## stat78 -4.082e-03 6.480e-03 -0.630 0.528704
## stat79 3.659e-04 6.454e-03 0.057 0.954787
## stat80 1.004e-02 6.456e-03 1.555 0.120110
## stat81 6.065e-03 6.446e-03 0.941 0.346849
## stat82 3.653e-03 6.390e-03 0.572 0.567588
## stat83 -3.894e-03 6.422e-03 -0.606 0.544268
## stat84 -3.232e-03 6.453e-03 -0.501 0.616516
## stat85 -6.129e-03 6.462e-03 -0.948 0.342996
## stat86 2.321e-03 6.460e-03 0.359 0.719345
## stat87 -1.301e-02 6.453e-03 -2.017 0.043779 *
## stat88 -8.228e-03 6.380e-03 -1.290 0.197231
## stat89 -8.411e-03 6.396e-03 -1.315 0.188556
## stat90 -5.208e-03 6.437e-03 -0.809 0.418486
## stat91 -1.303e-02 6.356e-03 -2.050 0.040384 *
## stat92 -1.683e-02 6.451e-03 -2.608 0.009119 **
## stat93 -3.401e-03 6.451e-03 -0.527 0.598028
## stat94 -7.454e-03 6.451e-03 -1.156 0.247926
## stat95 -2.965e-04 6.459e-03 -0.046 0.963388
## stat96 -7.695e-03 6.391e-03 -1.204 0.228681
## stat97 -1.005e-04 6.360e-03 -0.016 0.987392
## stat98 1.035e-01 6.311e-03 16.397 < 2e-16 ***
## stat99 3.297e-03 6.511e-03 0.506 0.612661
## stat100 1.544e-02 6.447e-03 2.395 0.016648 *
## stat101 -3.028e-03 6.471e-03 -0.468 0.639870
## stat102 1.842e-03 6.440e-03 0.286 0.774832
## stat103 -8.171e-03 6.498e-03 -1.257 0.208697
## stat104 -5.479e-03 6.401e-03 -0.856 0.392062
## stat105 6.867e-03 6.364e-03 1.079 0.280565
## stat106 -6.228e-03 6.382e-03 -0.976 0.329216
## stat107 9.862e-04 6.432e-03 0.153 0.878143
## stat108 -4.720e-03 6.436e-03 -0.733 0.463345
## stat109 4.534e-03 6.407e-03 0.708 0.479156
## stat110 -9.551e-02 6.391e-03 -14.944 < 2e-16 ***
## stat111 -3.954e-03 6.446e-03 -0.613 0.539646
## stat112 -2.024e-03 6.476e-03 -0.313 0.754666
## stat113 -2.050e-04 6.485e-03 -0.032 0.974782
## stat114 4.732e-03 6.409e-03 0.738 0.460367
## stat115 8.549e-03 6.418e-03 1.332 0.182895
## stat116 9.455e-03 6.456e-03 1.465 0.143087
## stat117 3.403e-03 6.469e-03 0.526 0.598865
## stat118 -9.630e-03 6.392e-03 -1.507 0.131934
## stat119 3.087e-03 6.428e-03 0.480 0.631117
## stat120 3.195e-04 6.367e-03 0.050 0.959983
## stat121 -8.602e-03 6.455e-03 -1.333 0.182725
## stat122 -5.817e-03 6.404e-03 -0.908 0.363741
## stat123 2.419e-03 6.510e-03 0.372 0.710167
## stat124 -6.335e-03 6.444e-03 -0.983 0.325557
## stat125 6.947e-03 6.443e-03 1.078 0.281011
## stat126 1.043e-02 6.382e-03 1.634 0.102241
## stat127 4.574e-03 6.422e-03 0.712 0.476364
## stat128 -1.078e-02 6.423e-03 -1.679 0.093225 .
## stat129 -5.211e-03 6.425e-03 -0.811 0.417400
## stat130 5.040e-03 6.479e-03 0.778 0.436649
## stat131 4.850e-03 6.447e-03 0.752 0.451909
## stat132 -4.909e-03 6.394e-03 -0.768 0.442675
## stat133 -3.181e-03 6.439e-03 -0.494 0.621323
## stat134 -6.248e-03 6.418e-03 -0.974 0.330334
## stat135 -5.217e-04 6.458e-03 -0.081 0.935620
## stat136 1.498e-03 6.467e-03 0.232 0.816828
## stat137 3.729e-03 6.416e-03 0.581 0.561159
## stat138 1.642e-03 6.424e-03 0.256 0.798308
## stat139 5.367e-03 6.446e-03 0.833 0.405074
## stat140 -1.504e-03 6.387e-03 -0.235 0.813898
## stat141 7.460e-03 6.377e-03 1.170 0.242119
## stat142 -2.202e-03 6.509e-03 -0.338 0.735093
## stat143 4.968e-03 6.470e-03 0.768 0.442609
## stat144 1.755e-02 6.428e-03 2.730 0.006349 **
## stat145 7.676e-04 6.482e-03 0.118 0.905745
## stat146 -1.712e-02 6.426e-03 -2.664 0.007743 **
## stat147 -5.558e-03 6.503e-03 -0.855 0.392782
## stat148 -9.068e-03 6.377e-03 -1.422 0.155050
## stat149 -9.940e-03 6.442e-03 -1.543 0.122894
## stat150 2.899e-03 6.476e-03 0.448 0.654443
## stat151 -3.044e-03 6.501e-03 -0.468 0.639631
## stat152 -4.849e-03 6.420e-03 -0.755 0.450051
## stat153 9.859e-04 6.504e-03 0.152 0.879510
## stat154 4.942e-04 6.518e-03 0.076 0.939568
## stat155 -1.985e-03 6.409e-03 -0.310 0.756707
## stat156 1.223e-02 6.469e-03 1.890 0.058778 .
## stat157 1.573e-03 6.416e-03 0.245 0.806359
## stat158 -3.395e-03 6.534e-03 -0.520 0.603402
## stat159 -7.670e-04 6.442e-03 -0.119 0.905229
## stat160 7.535e-04 6.499e-03 0.116 0.907696
## stat161 5.815e-03 6.455e-03 0.901 0.367714
## stat162 9.981e-05 6.380e-03 0.016 0.987519
## stat163 3.660e-03 6.502e-03 0.563 0.573490
## stat164 9.777e-03 6.476e-03 1.510 0.131148
## stat165 4.507e-03 6.403e-03 0.704 0.481538
## stat166 -3.907e-03 6.330e-03 -0.617 0.537096
## stat167 -9.456e-03 6.432e-03 -1.470 0.141580
## stat168 -5.324e-03 6.459e-03 -0.824 0.409773
## stat169 -2.612e-04 6.423e-03 -0.041 0.967565
## stat170 -4.085e-03 6.426e-03 -0.636 0.524981
## stat171 5.913e-03 6.514e-03 0.908 0.364089
## stat172 6.988e-03 6.416e-03 1.089 0.276128
## stat173 -7.858e-03 6.454e-03 -1.218 0.223441
## stat174 -5.624e-03 6.429e-03 -0.875 0.381777
## stat175 -5.600e-03 6.452e-03 -0.868 0.385519
## stat176 1.599e-03 6.439e-03 0.248 0.803927
## stat177 -4.052e-03 6.428e-03 -0.630 0.528515
## stat178 2.329e-03 6.499e-03 0.358 0.720106
## stat179 -2.716e-03 6.424e-03 -0.423 0.672422
## stat180 -7.453e-03 6.397e-03 -1.165 0.244080
## stat181 3.352e-03 6.467e-03 0.518 0.604250
## stat182 6.080e-03 6.480e-03 0.938 0.348185
## stat183 5.721e-03 6.440e-03 0.888 0.374423
## stat184 7.671e-03 6.491e-03 1.182 0.237341
## stat185 -3.219e-03 6.374e-03 -0.505 0.613561
## stat186 -7.090e-03 6.453e-03 -1.099 0.271981
## stat187 -1.418e-02 6.383e-03 -2.222 0.026314 *
## stat188 -8.074e-04 6.409e-03 -0.126 0.899750
## stat189 2.665e-03 6.420e-03 0.415 0.678097
## stat190 4.180e-03 6.373e-03 0.656 0.511936
## stat191 -1.135e-02 6.465e-03 -1.755 0.079312 .
## stat192 -6.346e-04 6.493e-03 -0.098 0.922139
## stat193 5.267e-03 6.523e-03 0.807 0.419413
## stat194 -2.616e-03 6.383e-03 -0.410 0.681971
## stat195 5.504e-03 6.412e-03 0.858 0.390665
## stat196 -1.342e-03 6.511e-03 -0.206 0.836706
## stat197 -1.765e-03 6.400e-03 -0.276 0.782699
## stat198 -1.019e-02 6.445e-03 -1.581 0.113852
## stat199 1.007e-02 6.372e-03 1.580 0.114059
## stat200 -5.205e-03 6.394e-03 -0.814 0.415666
## stat201 4.307e-03 6.430e-03 0.670 0.503007
## stat202 -4.267e-03 6.512e-03 -0.655 0.512326
## stat203 4.532e-04 6.404e-03 0.071 0.943588
## stat204 -1.218e-02 6.415e-03 -1.898 0.057703 .
## stat205 -8.533e-03 6.408e-03 -1.332 0.183036
## stat206 -7.150e-03 6.484e-03 -1.103 0.270213
## stat207 1.090e-02 6.468e-03 1.686 0.091935 .
## stat208 2.912e-03 6.438e-03 0.452 0.651031
## stat209 -2.892e-03 6.401e-03 -0.452 0.651478
## stat210 -2.436e-03 6.442e-03 -0.378 0.705316
## stat211 -6.395e-04 6.436e-03 -0.099 0.920853
## stat212 -3.340e-04 6.420e-03 -0.052 0.958507
## stat213 -2.571e-03 6.471e-03 -0.397 0.691137
## stat214 -1.014e-02 6.408e-03 -1.583 0.113459
## stat215 -1.026e-02 6.437e-03 -1.594 0.110898
## stat216 -4.468e-03 6.451e-03 -0.693 0.488536
## stat217 7.796e-03 6.435e-03 1.211 0.225768
## sqrt.x18 8.033e-01 2.464e-02 32.608 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8462 on 5761 degrees of freedom
## Multiple R-squared: 0.311, Adjusted R-squared: 0.2823
## F-statistic: 10.83 on 240 and 5761 DF, p-value: < 2.2e-16cd.full = plot.diagnostics(model.full, data.train)
## [1] "Number of data points that have Cook's D > 4/n: 309"
## [1] "Number of data points that have Cook's D > 1: 0"Checking with removal of high influence points
high.cd = names(cd.full[cd.full > 4/nrow(data.train)])
data.train2 = data.train[!(rownames(data.train)) %in% high.cd,]
model.full2 = lm(formula , data.train2)
summary(model.full2)##
## Call:
## lm(formula = formula, data = data.train2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.80383 -0.50441 -0.04955 0.50249 1.84196
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -4.003e+00 2.165e-01 -18.486 < 2e-16 ***
## x1 -2.074e-02 1.489e-02 -1.392 0.163862
## x2 7.454e-03 9.496e-03 0.785 0.432513
## x3 7.589e-05 2.581e-03 0.029 0.976541
## x4 -1.402e-03 2.037e-04 -6.882 6.57e-12 ***
## x5 1.239e-02 6.692e-03 1.851 0.064221 .
## x6 -8.395e-03 1.352e-02 -0.621 0.534687
## x7 3.586e-01 1.452e-02 24.697 < 2e-16 ***
## x8 1.128e-02 3.366e-03 3.351 0.000812 ***
## x9 1.005e-01 7.504e-03 13.387 < 2e-16 ***
## x10 4.573e-02 6.990e-03 6.543 6.58e-11 ***
## x11 5.270e+06 1.678e+06 3.140 0.001700 **
## x12 -2.917e-03 4.240e-03 -0.688 0.491525
## x13 5.543e-03 1.694e-03 3.272 0.001075 **
## x14 -1.038e-02 7.325e-03 -1.418 0.156350
## x15 3.775e-03 6.997e-03 0.540 0.589524
## x16 2.442e-02 4.840e-03 5.045 4.67e-07 ***
## x17 3.346e-02 7.405e-03 4.518 6.36e-06 ***
## x19 7.000e-03 3.739e-03 1.872 0.061253 .
## x20 -1.224e-02 2.610e-02 -0.469 0.639076
## x21 4.915e-03 9.596e-04 5.122 3.13e-07 ***
## x22 -1.914e-02 7.787e-03 -2.457 0.014031 *
## x23 8.417e-04 7.439e-03 0.113 0.909916
## stat1 -3.853e-03 5.610e-03 -0.687 0.492242
## stat2 8.196e-03 5.560e-03 1.474 0.140503
## stat3 1.130e-02 5.648e-03 2.001 0.045477 *
## stat4 -2.013e-02 5.639e-03 -3.570 0.000360 ***
## stat5 -8.208e-03 5.657e-03 -1.451 0.146849
## stat6 -9.100e-03 5.619e-03 -1.619 0.105418
## stat7 3.241e-03 5.618e-03 0.577 0.563996
## stat8 -5.448e-03 5.643e-03 -0.965 0.334410
## stat9 2.790e-04 5.613e-03 0.050 0.960361
## stat10 -6.538e-03 5.594e-03 -1.169 0.242538
## stat11 -1.284e-02 5.673e-03 -2.264 0.023627 *
## stat12 5.048e-03 5.595e-03 0.902 0.366892
## stat13 -7.928e-03 5.623e-03 -1.410 0.158614
## stat14 -3.069e-02 5.585e-03 -5.495 4.09e-08 ***
## stat15 -1.508e-02 5.586e-03 -2.699 0.006967 **
## stat16 -2.924e-05 5.599e-03 -0.005 0.995833
## stat17 -4.747e-03 5.565e-03 -0.853 0.393659
## stat18 -3.256e-03 5.555e-03 -0.586 0.557818
## stat19 4.893e-03 5.609e-03 0.872 0.383055
## stat20 -4.002e-03 5.637e-03 -0.710 0.477755
## stat21 9.920e-04 5.651e-03 0.176 0.860648
## stat22 -5.107e-03 5.641e-03 -0.905 0.365318
## stat23 1.521e-02 5.619e-03 2.708 0.006796 **
## stat24 -1.284e-02 5.634e-03 -2.279 0.022726 *
## stat25 -1.015e-02 5.628e-03 -1.803 0.071448 .
## stat26 -8.477e-03 5.631e-03 -1.505 0.132280
## stat27 2.988e-03 5.621e-03 0.531 0.595098
## stat28 2.213e-03 5.631e-03 0.393 0.694366
## stat29 5.510e-03 5.640e-03 0.977 0.328603
## stat30 3.500e-03 5.679e-03 0.616 0.537796
## stat31 -1.977e-03 5.650e-03 -0.350 0.726425
## stat32 5.982e-03 5.700e-03 1.049 0.294030
## stat33 -1.248e-02 5.627e-03 -2.218 0.026594 *
## stat34 7.269e-03 5.645e-03 1.288 0.197923
## stat35 -1.085e-02 5.605e-03 -1.936 0.052932 .
## stat36 6.848e-03 5.569e-03 1.230 0.218820
## stat37 -1.293e-02 5.686e-03 -2.274 0.022997 *
## stat38 1.099e-02 5.608e-03 1.959 0.050138 .
## stat39 -7.185e-03 5.565e-03 -1.291 0.196698
## stat40 2.234e-03 5.627e-03 0.397 0.691357
## stat41 -1.747e-02 5.579e-03 -3.131 0.001752 **
## stat42 -6.770e-03 5.622e-03 -1.204 0.228515
## stat43 -1.183e-02 5.635e-03 -2.100 0.035791 *
## stat44 7.781e-03 5.584e-03 1.394 0.163514
## stat45 -5.918e-03 5.619e-03 -1.053 0.292312
## stat46 9.009e-03 5.633e-03 1.599 0.109803
## stat47 6.639e-03 5.671e-03 1.171 0.241805
## stat48 1.200e-03 5.612e-03 0.214 0.830734
## stat49 2.438e-03 5.579e-03 0.437 0.662177
## stat50 1.406e-03 5.588e-03 0.252 0.801411
## stat51 4.526e-03 5.608e-03 0.807 0.419687
## stat52 -2.646e-03 5.629e-03 -0.470 0.638256
## stat53 -1.789e-03 5.663e-03 -0.316 0.752111
## stat54 -9.090e-03 5.635e-03 -1.613 0.106803
## stat55 5.982e-03 5.554e-03 1.077 0.281518
## stat56 4.310e-04 5.639e-03 0.076 0.939078
## stat57 3.015e-03 5.576e-03 0.541 0.588775
## stat58 1.283e-04 5.575e-03 0.023 0.981636
## stat59 -5.323e-04 5.610e-03 -0.095 0.924399
## stat60 1.631e-02 5.627e-03 2.898 0.003769 **
## stat61 -1.952e-03 5.652e-03 -0.345 0.729911
## stat62 -9.415e-03 5.617e-03 -1.676 0.093759 .
## stat63 2.096e-03 5.612e-03 0.374 0.708787
## stat64 4.352e-03 5.571e-03 0.781 0.434701
## stat65 1.373e-03 5.630e-03 0.244 0.807344
## stat66 6.133e-03 5.685e-03 1.079 0.280719
## stat67 3.300e-03 5.653e-03 0.584 0.559333
## stat68 -2.003e-03 5.637e-03 -0.355 0.722404
## stat69 -1.223e-03 5.635e-03 -0.217 0.828139
## stat70 2.681e-03 5.603e-03 0.479 0.632267
## stat71 4.478e-03 5.602e-03 0.799 0.424170
## stat72 -5.647e-03 5.613e-03 -1.006 0.314444
## stat73 6.891e-03 5.638e-03 1.222 0.221680
## stat74 -2.848e-03 5.658e-03 -0.503 0.614699
## stat75 3.292e-03 5.655e-03 0.582 0.560436
## stat76 9.730e-03 5.600e-03 1.737 0.082356 .
## stat77 9.277e-04 5.638e-03 0.165 0.869314
## stat78 -7.300e-03 5.645e-03 -1.293 0.195959
## stat79 1.791e-03 5.634e-03 0.318 0.750553
## stat80 1.115e-02 5.642e-03 1.976 0.048201 *
## stat81 6.054e-03 5.637e-03 1.074 0.282914
## stat82 1.846e-03 5.576e-03 0.331 0.740645
## stat83 -1.998e-03 5.615e-03 -0.356 0.721978
## stat84 -8.435e-03 5.639e-03 -1.496 0.134769
## stat85 -9.330e-03 5.651e-03 -1.651 0.098817 .
## stat86 5.128e-03 5.654e-03 0.907 0.364438
## stat87 -1.003e-02 5.629e-03 -1.782 0.074801 .
## stat88 -4.986e-03 5.585e-03 -0.893 0.372062
## stat89 -1.785e-03 5.601e-03 -0.319 0.750022
## stat90 -8.185e-03 5.631e-03 -1.454 0.146118
## stat91 -1.080e-02 5.542e-03 -1.949 0.051367 .
## stat92 -1.243e-02 5.639e-03 -2.204 0.027584 *
## stat93 -1.082e-03 5.669e-03 -0.191 0.848710
## stat94 -6.814e-03 5.628e-03 -1.211 0.226031
## stat95 2.179e-03 5.653e-03 0.385 0.699942
## stat96 -6.876e-03 5.583e-03 -1.232 0.218182
## stat97 1.612e-03 5.559e-03 0.290 0.771810
## stat98 1.076e-01 5.523e-03 19.480 < 2e-16 ***
## stat99 7.657e-03 5.688e-03 1.346 0.178317
## stat100 1.745e-02 5.622e-03 3.104 0.001916 **
## stat101 2.793e-04 5.648e-03 0.049 0.960555
## stat102 1.998e-03 5.620e-03 0.355 0.722266
## stat103 -1.086e-02 5.677e-03 -1.913 0.055826 .
## stat104 -5.808e-03 5.605e-03 -1.036 0.300165
## stat105 8.630e-03 5.569e-03 1.549 0.121323
## stat106 -6.706e-03 5.564e-03 -1.205 0.228188
## stat107 1.987e-03 5.622e-03 0.353 0.723783
## stat108 -7.122e-03 5.631e-03 -1.265 0.205989
## stat109 2.914e-03 5.604e-03 0.520 0.603059
## stat110 -1.019e-01 5.582e-03 -18.251 < 2e-16 ***
## stat111 -5.994e-03 5.630e-03 -1.065 0.287142
## stat112 4.543e-04 5.656e-03 0.080 0.935986
## stat113 1.047e-03 5.661e-03 0.185 0.853230
## stat114 6.157e-03 5.598e-03 1.100 0.271490
## stat115 1.112e-02 5.613e-03 1.981 0.047668 *
## stat116 1.177e-02 5.643e-03 2.086 0.037025 *
## stat117 4.778e-03 5.647e-03 0.846 0.397480
## stat118 -5.691e-03 5.578e-03 -1.020 0.307600
## stat119 1.202e-02 5.613e-03 2.142 0.032217 *
## stat120 -2.687e-03 5.560e-03 -0.483 0.628915
## stat121 -1.194e-02 5.649e-03 -2.113 0.034659 *
## stat122 -1.137e-02 5.603e-03 -2.029 0.042477 *
## stat123 9.707e-03 5.689e-03 1.706 0.088027 .
## stat124 -1.144e-02 5.628e-03 -2.033 0.042143 *
## stat125 5.262e-03 5.639e-03 0.933 0.350814
## stat126 1.454e-02 5.572e-03 2.610 0.009089 **
## stat127 2.341e-03 5.612e-03 0.417 0.676615
## stat128 -1.099e-02 5.603e-03 -1.961 0.049971 *
## stat129 -8.604e-03 5.610e-03 -1.534 0.125188
## stat130 2.436e-03 5.657e-03 0.431 0.666753
## stat131 4.304e-03 5.620e-03 0.766 0.443790
## stat132 -8.439e-03 5.589e-03 -1.510 0.131130
## stat133 -1.420e-03 5.638e-03 -0.252 0.801227
## stat134 -6.782e-03 5.607e-03 -1.210 0.226469
## stat135 3.862e-04 5.640e-03 0.068 0.945409
## stat136 -3.635e-03 5.653e-03 -0.643 0.520213
## stat137 8.668e-03 5.592e-03 1.550 0.121175
## stat138 2.497e-03 5.625e-03 0.444 0.657161
## stat139 8.083e-03 5.632e-03 1.435 0.151291
## stat140 2.031e-03 5.566e-03 0.365 0.715170
## stat141 1.033e-02 5.566e-03 1.856 0.063477 .
## stat142 -3.677e-04 5.689e-03 -0.065 0.948469
## stat143 2.071e-03 5.654e-03 0.366 0.714207
## stat144 1.591e-02 5.630e-03 2.825 0.004740 **
## stat145 5.627e-04 5.663e-03 0.099 0.920853
## stat146 -1.275e-02 5.618e-03 -2.269 0.023330 *
## stat147 -1.082e-02 5.688e-03 -1.902 0.057215 .
## stat148 -8.708e-03 5.580e-03 -1.561 0.118643
## stat149 -1.329e-02 5.640e-03 -2.356 0.018511 *
## stat150 -1.085e-03 5.669e-03 -0.191 0.848275
## stat151 4.775e-04 5.703e-03 0.084 0.933276
## stat152 -4.765e-03 5.600e-03 -0.851 0.394790
## stat153 4.585e-03 5.681e-03 0.807 0.419613
## stat154 3.998e-03 5.706e-03 0.701 0.483523
## stat155 3.011e-03 5.608e-03 0.537 0.591346
## stat156 1.558e-02 5.642e-03 2.761 0.005778 **
## stat157 -1.267e-03 5.601e-03 -0.226 0.820985
## stat158 2.363e-03 5.714e-03 0.414 0.679191
## stat159 3.291e-04 5.618e-03 0.059 0.953292
## stat160 3.145e-03 5.678e-03 0.554 0.579673
## stat161 6.998e-03 5.642e-03 1.240 0.214871
## stat162 -1.415e-04 5.554e-03 -0.025 0.979682
## stat163 8.284e-03 5.698e-03 1.454 0.146039
## stat164 6.614e-03 5.664e-03 1.168 0.242966
## stat165 4.518e-03 5.590e-03 0.808 0.418999
## stat166 -4.153e-03 5.520e-03 -0.752 0.451917
## stat167 -1.633e-02 5.623e-03 -2.904 0.003694 **
## stat168 -3.490e-03 5.640e-03 -0.619 0.536003
## stat169 2.155e-03 5.627e-03 0.383 0.701763
## stat170 -5.804e-03 5.615e-03 -1.034 0.301355
## stat171 2.541e-03 5.704e-03 0.446 0.655955
## stat172 1.102e-02 5.597e-03 1.969 0.049037 *
## stat173 -1.873e-03 5.645e-03 -0.332 0.740024
## stat174 -1.804e-03 5.616e-03 -0.321 0.748079
## stat175 -6.700e-03 5.626e-03 -1.191 0.233708
## stat176 -4.911e-03 5.630e-03 -0.872 0.383113
## stat177 -1.645e-02 5.607e-03 -2.934 0.003362 **
## stat178 4.062e-03 5.671e-03 0.716 0.473818
## stat179 -5.629e-03 5.615e-03 -1.003 0.316128
## stat180 -3.202e-03 5.602e-03 -0.572 0.567603
## stat181 7.103e-03 5.643e-03 1.259 0.208152
## stat182 1.079e-02 5.670e-03 1.904 0.056988 .
## stat183 4.880e-03 5.623e-03 0.868 0.385464
## stat184 9.128e-03 5.663e-03 1.612 0.107029
## stat185 6.862e-04 5.579e-03 0.123 0.902115
## stat186 -7.738e-06 5.632e-03 -0.001 0.998904
## stat187 -8.507e-03 5.576e-03 -1.526 0.127175
## stat188 -8.726e-04 5.601e-03 -0.156 0.876201
## stat189 -1.784e-03 5.610e-03 -0.318 0.750444
## stat190 3.340e-03 5.577e-03 0.599 0.549319
## stat191 -1.282e-02 5.639e-03 -2.273 0.023057 *
## stat192 -9.478e-04 5.664e-03 -0.167 0.867104
## stat193 1.274e-02 5.708e-03 2.231 0.025696 *
## stat194 -4.000e-03 5.598e-03 -0.715 0.474948
## stat195 3.797e-03 5.615e-03 0.676 0.498879
## stat196 -6.950e-03 5.683e-03 -1.223 0.221372
## stat197 -2.530e-03 5.599e-03 -0.452 0.651454
## stat198 -9.806e-03 5.626e-03 -1.743 0.081410 .
## stat199 5.166e-03 5.567e-03 0.928 0.353450
## stat200 -1.407e-03 5.606e-03 -0.251 0.801797
## stat201 7.135e-03 5.623e-03 1.269 0.204561
## stat202 -3.784e-03 5.693e-03 -0.665 0.506323
## stat203 5.186e-03 5.598e-03 0.926 0.354240
## stat204 -6.607e-03 5.605e-03 -1.179 0.238613
## stat205 -1.837e-03 5.598e-03 -0.328 0.742756
## stat206 -9.401e-03 5.657e-03 -1.662 0.096602 .
## stat207 1.318e-02 5.651e-03 2.333 0.019678 *
## stat208 3.992e-03 5.640e-03 0.708 0.479038
## stat209 4.729e-05 5.585e-03 0.008 0.993244
## stat210 -8.971e-03 5.622e-03 -1.596 0.110603
## stat211 -1.713e-03 5.628e-03 -0.304 0.760867
## stat212 1.782e-03 5.616e-03 0.317 0.750952
## stat213 -1.030e-03 5.642e-03 -0.182 0.855230
## stat214 -5.655e-03 5.605e-03 -1.009 0.313104
## stat215 -9.394e-03 5.626e-03 -1.670 0.095058 .
## stat216 -3.165e-03 5.624e-03 -0.563 0.573668
## stat217 1.652e-03 5.617e-03 0.294 0.768693
## sqrt.x18 8.385e-01 2.153e-02 38.945 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.719 on 5452 degrees of freedom
## Multiple R-squared: 0.41, Adjusted R-squared: 0.384
## F-statistic: 15.79 on 240 and 5452 DF, p-value: < 2.2e-16cd.full2 = plot.diagnostics(model.full2, data.train2)
## [1] "Number of data points that have Cook's D > 4/n: 246"
## [1] "Number of data points that have Cook's D > 1: 0"# much more normal residuals than before.
# Checking to see if distributions are different and if so whcih variables
# High Leverage Plot
plotData = data.train %>%
rownames_to_column() %>%
mutate(type=ifelse(rowname %in% high.cd,'High','Normal')) %>%
dplyr::select(type,target=one_of(label.names))
ggplot(data=plotData, aes(x=type,y=target)) +
geom_boxplot(fill='light blue',outlier.shape=NA) +
scale_y_continuous(name="Target Variable Values") +
theme_light() +
ggtitle('Distribution of High Leverage Points and Normal Points')
plotData = data.train %>%
rownames_to_column() %>%
mutate(type=ifelse(rowname %in% high.cd,'High','Normal')) %>%
dplyr::select(type,one_of(feature.names))# 2 sample t-tests
comp.test = lapply(dplyr::select(plotData, one_of(feature.names)), function(x) t.test(x ~ plotData$type, var.equal = TRUE))
sig.comp = list.filter(comp.test, p.value < 0.05)
sapply(sig.comp, function(x) x[['p.value']])## x4 x7 stat26 stat38 stat47
## 1.236797e-02 3.013279e-02 1.928432e-02 5.933891e-03 2.745352e-02
## stat74 stat93 stat98 stat110 stat144
## 4.948249e-02 5.471169e-03 8.687036e-11 1.316119e-07 1.474569e-02
## stat146 stat170 stat174 stat200
## 3.735914e-02 2.366058e-02 3.427287e-02 1.623301e-02# Distribution (box) Plots
mm = melt(plotData, id=c('type'))
ggplot(mm) +
geom_boxplot(aes(x=type, y=value))+
facet_wrap(~variable, ncol=10, scales = 'free') +
ggtitle('Distribution of High Leverage Points and Normal Points')
ggsave('comparison.jpeg', width =50, height = 400, units='cm',limitsize = FALSE)Grand Means Model
model.null = lm(grand.mean.formula, data.train)model.null2 = lm(grand.mean.formula, data.train2)Variable Selection
Basic: http://www.stat.columbia.edu/~martin/W2024/R10.pdf Cross Validation + Other Metrics: http://www.sthda.com/english/articles/37-model-selection-essentials-in-r/154-stepwise-regression-essentials-in-r/
Forward Selection (w/ full train)
Train
if (algo.forward == TRUE){
t1 = Sys.time()
model.forward = step(model.null, scope=list(lower=model.null, upper=model.full), direction="forward", trace = 0)
print(summary(model.forward))
t2 = Sys.time()
print (paste("Time taken for Forward Selection: ",t2-t1, sep = ""))
plot.diagnostics(model.forward, data.train)
}Test
if (algo.forward == TRUE){
test.model(model.forward, data.test, "Forward Selection")
}Forward Selection (w/ filtered train)
Train
if (algo.forward == TRUE){
t1 = Sys.time()
model.forward2 = step(model.null2, scope=list(lower=model.null2, upper=model.full2), direction="forward", trace = 0)
print(summary(model.forward2))
t2 = Sys.time()
print (paste("Time taken for Forward Selection: ",t2-t1, sep = ""))
plot.diagnostics(model.forward2, data.train2)
}Test
if (algo.forward == TRUE){
test.model(model.forward2, data.test, "Forward Selection (2)")
}Forward Selection with CV (w/ full train)
Train
if (algo.forward.caret == TRUE){
set.seed(1)
returned = train.caret.glmselect(formula = formula
, data = data.train
, method = "leapForward"
, feature.names = feature.names)
model.forward = returned$model
id = returned$id
}## Aggregating results
## Selecting tuning parameters
## Fitting nvmax = 11 on full training set
## nvmax RMSE Rsquared MAE RMSESD RsquaredSD MAESD
## 1 1 0.9290962 0.1383305 0.7452032 0.02166207 0.04198587 0.014135754
## 2 2 0.9013733 0.1870866 0.7246099 0.01861952 0.03823400 0.010184999
## 3 3 0.8828368 0.2193324 0.7063992 0.01966356 0.03628172 0.013394655
## 4 4 0.8674643 0.2460619 0.6887616 0.01704415 0.03510583 0.008406016
## 5 5 0.8579880 0.2627745 0.6814284 0.02179327 0.04052670 0.012261767
## 6 6 0.8569684 0.2645498 0.6802092 0.02203645 0.04140563 0.012736876
## 7 7 0.8563457 0.2657719 0.6808046 0.02394403 0.04272918 0.013719134
## 8 8 0.8534905 0.2706668 0.6783829 0.02287015 0.04290888 0.012749598
## 9 9 0.8529722 0.2715124 0.6777699 0.02320165 0.04311993 0.013391833
## 10 10 0.8527848 0.2718440 0.6780023 0.02361486 0.04282652 0.013573185
## 11 11 0.8508870 0.2750101 0.6760872 0.02343404 0.04253772 0.013138789
## 12 12 0.8511750 0.2745439 0.6765241 0.02288446 0.04185114 0.012393757
## 13 13 0.8517134 0.2736653 0.6773621 0.02284295 0.04132396 0.012437282
## 14 14 0.8521985 0.2728552 0.6776070 0.02318661 0.04172786 0.012554160
## 15 15 0.8523841 0.2725331 0.6784420 0.02315885 0.04149192 0.012613788
## 16 16 0.8525513 0.2722630 0.6784920 0.02260269 0.04071101 0.012576496
## 17 17 0.8529977 0.2716226 0.6786935 0.02270362 0.04171689 0.012658149
## 18 18 0.8542352 0.2695788 0.6798453 0.02300600 0.04216666 0.013038508
## 19 19 0.8540150 0.2699300 0.6797931 0.02318615 0.04224529 0.013164103
## 20 20 0.8543074 0.2694820 0.6799731 0.02327857 0.04326866 0.013380958
## 21 21 0.8541718 0.2697847 0.6799596 0.02338701 0.04281288 0.012972946
## 22 22 0.8538485 0.2703269 0.6801116 0.02325171 0.04246082 0.013013846
## 23 23 0.8540525 0.2699838 0.6804112 0.02348951 0.04322998 0.013439718
## 24 24 0.8542235 0.2696554 0.6802333 0.02293015 0.04221332 0.012719398
## 25 25 0.8540148 0.2699457 0.6796657 0.02290768 0.04237085 0.012463534
## 26 26 0.8542857 0.2694675 0.6797957 0.02275447 0.04175079 0.012054184
## 27 27 0.8547728 0.2686518 0.6800886 0.02272751 0.04164389 0.012394635
## 28 28 0.8549054 0.2684144 0.6803033 0.02199797 0.04096413 0.011766341
## 29 29 0.8548186 0.2685977 0.6801696 0.02197216 0.04009868 0.011406251
## 30 30 0.8549068 0.2684762 0.6803886 0.02199649 0.03958926 0.011365407
## 31 31 0.8546658 0.2688977 0.6800989 0.02180320 0.03932277 0.011210652
## 32 32 0.8547160 0.2688080 0.6799741 0.02185699 0.03922505 0.010993179
## 33 33 0.8548176 0.2686519 0.6797295 0.02197067 0.03871603 0.011338790
## 34 34 0.8547794 0.2687560 0.6796216 0.02221011 0.03927036 0.011415235
## 35 35 0.8548506 0.2686375 0.6799361 0.02181091 0.03894750 0.011317318
## 36 36 0.8554041 0.2677701 0.6801746 0.02192686 0.03879174 0.011130982
## 37 37 0.8555087 0.2676602 0.6804876 0.02233199 0.03924478 0.011923968
## 38 38 0.8559311 0.2669784 0.6808720 0.02206198 0.03899271 0.011453150
## 39 39 0.8558155 0.2671533 0.6807627 0.02187636 0.03848619 0.011281950
## 40 40 0.8559894 0.2668621 0.6808054 0.02184685 0.03840533 0.011168598
## 41 41 0.8563824 0.2662562 0.6810874 0.02174172 0.03807427 0.010831263
## 42 42 0.8566350 0.2657774 0.6811238 0.02104020 0.03711238 0.010395133
## 43 43 0.8566226 0.2657972 0.6810760 0.02149397 0.03751134 0.010860892
## 44 44 0.8565980 0.2658459 0.6809241 0.02118643 0.03748940 0.010770790
## 45 45 0.8569760 0.2652600 0.6811146 0.02124494 0.03706930 0.010920502
## 46 46 0.8573623 0.2647052 0.6814429 0.02146800 0.03753365 0.011036650
## 47 47 0.8570724 0.2652069 0.6811420 0.02165282 0.03761367 0.011110274
## 48 48 0.8577161 0.2641628 0.6816867 0.02194901 0.03794519 0.011219820
## 49 49 0.8578362 0.2639640 0.6818130 0.02203398 0.03824144 0.011476496
## 50 50 0.8584853 0.2629305 0.6821517 0.02220355 0.03853874 0.011672943
## 51 51 0.8587093 0.2625723 0.6819704 0.02229359 0.03863962 0.011769751
## 52 52 0.8591956 0.2618083 0.6823571 0.02263500 0.03878790 0.011707581
## 53 53 0.8593379 0.2615810 0.6824007 0.02255288 0.03907664 0.011560892
## 54 54 0.8596900 0.2610742 0.6826549 0.02274893 0.03974641 0.011842914
## 55 55 0.8598219 0.2608948 0.6828240 0.02284957 0.03987481 0.011731767
## 56 56 0.8599794 0.2606597 0.6830468 0.02349799 0.04044514 0.012164682
## 57 57 0.8600886 0.2605014 0.6831561 0.02338335 0.04037923 0.012193899
## 58 58 0.8602529 0.2602251 0.6833835 0.02312299 0.03995030 0.011948017
## 59 59 0.8604175 0.2599717 0.6834951 0.02318162 0.04001172 0.011912458
## 60 60 0.8607766 0.2594320 0.6838963 0.02333166 0.03991449 0.011676456
## 61 61 0.8609864 0.2590818 0.6841748 0.02296370 0.03956210 0.011490966
## 62 62 0.8610687 0.2589812 0.6844362 0.02300207 0.03907630 0.011462493
## 63 63 0.8610314 0.2590310 0.6843547 0.02330899 0.03887315 0.011736920
## 64 64 0.8612663 0.2587358 0.6845447 0.02359907 0.03923559 0.012054913
## 65 65 0.8615031 0.2583652 0.6847471 0.02354704 0.03912432 0.012108481
## 66 66 0.8612308 0.2588001 0.6847008 0.02336067 0.03882306 0.011682746
## 67 67 0.8611993 0.2588833 0.6845935 0.02358232 0.03949473 0.011758230
## 68 68 0.8615160 0.2583688 0.6850614 0.02348977 0.03942843 0.011661822
## 69 69 0.8616260 0.2581953 0.6853684 0.02335020 0.03942954 0.011809502
## 70 70 0.8618400 0.2578550 0.6854117 0.02321225 0.03932591 0.011719962
## 71 71 0.8621513 0.2574261 0.6855382 0.02327991 0.03929257 0.011835079
## 72 72 0.8623577 0.2571308 0.6857160 0.02329707 0.03954206 0.011921347
## 73 73 0.8621682 0.2574697 0.6856769 0.02347616 0.03983259 0.012055988
## 74 74 0.8620750 0.2575968 0.6857381 0.02314256 0.03959652 0.011932931
## 75 75 0.8620065 0.2577401 0.6857659 0.02312727 0.03963903 0.011979461
## 76 76 0.8621628 0.2575154 0.6859593 0.02306146 0.03935705 0.011889673
## 77 77 0.8620860 0.2576435 0.6858563 0.02309965 0.03934620 0.011831114
## 78 78 0.8621693 0.2575390 0.6861204 0.02308133 0.03925204 0.011991342
## 79 79 0.8621404 0.2575706 0.6863324 0.02325144 0.03911904 0.012073742
## 80 80 0.8624706 0.2570990 0.6865466 0.02309762 0.03905190 0.012028117
## 81 81 0.8626862 0.2567576 0.6868265 0.02316362 0.03914279 0.012086522
## 82 82 0.8627285 0.2566763 0.6868049 0.02311977 0.03894001 0.012018600
## 83 83 0.8627722 0.2566312 0.6869627 0.02268904 0.03888316 0.011832450
## 84 84 0.8628117 0.2565505 0.6869915 0.02246128 0.03854674 0.011556406
## 85 85 0.8628648 0.2564948 0.6870910 0.02266817 0.03851121 0.012000307
## 86 86 0.8627977 0.2566012 0.6868223 0.02238742 0.03851067 0.011806893
## 87 87 0.8627776 0.2566741 0.6866793 0.02232002 0.03879629 0.011831915
## 88 88 0.8632343 0.2560114 0.6870997 0.02270213 0.03949521 0.012210782
## 89 89 0.8632723 0.2559746 0.6870332 0.02285965 0.03943081 0.012237016
## 90 90 0.8632977 0.2559541 0.6870014 0.02253992 0.03914520 0.012093311
## 91 91 0.8632101 0.2560845 0.6870284 0.02251315 0.03945611 0.012088822
## 92 92 0.8633732 0.2558384 0.6870681 0.02254282 0.03947916 0.012160370
## 93 93 0.8638109 0.2551524 0.6874736 0.02261511 0.03921647 0.012229534
## 94 94 0.8640371 0.2548289 0.6876399 0.02264787 0.03923498 0.012062999
## 95 95 0.8639810 0.2549298 0.6875175 0.02267471 0.03933478 0.012263835
## 96 96 0.8640199 0.2548857 0.6874447 0.02264547 0.03934333 0.011901472
## 97 97 0.8640623 0.2548393 0.6875210 0.02277264 0.03938409 0.012018802
## 98 98 0.8642027 0.2546242 0.6876506 0.02271281 0.03925834 0.011953740
## 99 99 0.8642223 0.2545938 0.6877688 0.02287490 0.03930092 0.012128978
## 100 100 0.8643953 0.2543239 0.6879567 0.02283680 0.03910614 0.012122823
## 101 101 0.8642898 0.2545060 0.6876635 0.02294560 0.03929611 0.012273775
## 102 102 0.8643984 0.2543533 0.6878161 0.02317186 0.03945902 0.012556138
## 103 103 0.8644414 0.2542932 0.6878291 0.02317741 0.03992543 0.012637911
## 104 104 0.8644312 0.2543128 0.6878634 0.02311180 0.04024388 0.012656844
## 105 105 0.8645867 0.2541142 0.6877943 0.02305129 0.04021094 0.012529866
## 106 106 0.8645211 0.2542035 0.6877694 0.02284128 0.04017092 0.012388908
## 107 107 0.8644295 0.2543775 0.6876091 0.02302157 0.04058668 0.012705034
## 108 108 0.8642918 0.2545693 0.6874994 0.02290152 0.04049468 0.012778650
## 109 109 0.8642588 0.2546218 0.6874266 0.02281878 0.04008472 0.012662462
## 110 110 0.8641358 0.2548169 0.6872850 0.02278611 0.03985671 0.012641131
## 111 111 0.8640490 0.2549749 0.6871702 0.02288256 0.04005688 0.012874742
## 112 112 0.8640439 0.2549893 0.6870929 0.02292379 0.04027497 0.012895991
## 113 113 0.8638233 0.2553571 0.6868187 0.02298608 0.04005399 0.012844863
## 114 114 0.8638847 0.2552265 0.6868735 0.02283827 0.03995297 0.012623877
## 115 115 0.8640994 0.2549156 0.6870200 0.02265917 0.03990577 0.012567007
## 116 116 0.8643432 0.2545373 0.6870965 0.02275910 0.03988876 0.012499155
## 117 117 0.8644913 0.2543263 0.6871455 0.02290318 0.03994050 0.012747046
## 118 118 0.8646037 0.2541825 0.6872131 0.02299817 0.04021151 0.012861606
## 119 119 0.8647883 0.2539053 0.6873866 0.02301958 0.04025596 0.012959759
## 120 120 0.8648420 0.2538341 0.6875101 0.02288677 0.04005430 0.012809239
## 121 121 0.8650588 0.2534951 0.6877688 0.02315420 0.04027291 0.013003466
## 122 122 0.8650664 0.2534893 0.6879085 0.02329925 0.04015560 0.013005883
## 123 123 0.8650870 0.2534768 0.6878294 0.02317109 0.04004659 0.012849221
## 124 124 0.8651706 0.2533624 0.6878983 0.02321733 0.04012785 0.012887384
## 125 125 0.8650079 0.2536246 0.6878948 0.02312456 0.04014860 0.012881596
## 126 126 0.8648326 0.2539158 0.6878239 0.02314649 0.04007277 0.012846072
## 127 127 0.8649503 0.2537337 0.6879778 0.02307285 0.04013759 0.012948779
## 128 128 0.8648631 0.2538768 0.6879572 0.02281031 0.03999860 0.012862436
## 129 129 0.8649251 0.2537929 0.6880771 0.02265131 0.03985246 0.012829841
## 130 130 0.8648339 0.2539362 0.6880271 0.02277664 0.03981192 0.012743900
## 131 131 0.8648512 0.2539416 0.6879300 0.02276978 0.03984771 0.012616150
## 132 132 0.8648048 0.2540291 0.6878424 0.02285107 0.03997839 0.012855128
## 133 133 0.8648641 0.2539349 0.6878735 0.02278441 0.03965723 0.012807189
## 134 134 0.8648208 0.2540066 0.6877914 0.02291047 0.03970202 0.013005217
## 135 135 0.8650188 0.2537317 0.6879071 0.02308153 0.03996177 0.013173714
## 136 136 0.8650103 0.2537216 0.6879409 0.02297190 0.03993806 0.013203914
## 137 137 0.8651462 0.2535217 0.6880732 0.02289293 0.03983206 0.013104625
## 138 138 0.8650313 0.2537122 0.6880403 0.02277634 0.04009320 0.013010420
## 139 139 0.8649779 0.2537800 0.6880651 0.02279724 0.04017715 0.013074404
## 140 140 0.8649062 0.2538843 0.6880241 0.02275770 0.04016311 0.013102834
## 141 141 0.8650169 0.2537294 0.6881171 0.02278117 0.04005702 0.013211692
## 142 142 0.8650306 0.2537039 0.6880975 0.02278478 0.04009348 0.013272763
## 143 143 0.8649787 0.2537859 0.6880701 0.02269098 0.04010051 0.013228295
## 144 144 0.8649252 0.2538728 0.6879425 0.02262159 0.03977474 0.013045760
## 145 145 0.8649771 0.2537971 0.6879181 0.02269770 0.03962796 0.013052834
## 146 146 0.8650057 0.2537733 0.6879637 0.02260088 0.03964417 0.013011851
## 147 147 0.8650938 0.2536399 0.6879479 0.02266791 0.03975614 0.013160245
## 148 148 0.8650924 0.2536764 0.6880071 0.02282070 0.03982985 0.013341775
## 149 149 0.8650850 0.2537045 0.6880012 0.02283697 0.03991278 0.013431125
## 150 150 0.8650554 0.2537385 0.6880047 0.02284209 0.03983081 0.013447539
## 151 151 0.8651231 0.2536358 0.6880233 0.02275608 0.03959060 0.013296807
## 152 152 0.8651102 0.2536548 0.6880271 0.02274299 0.03944983 0.013372629
## 153 153 0.8650833 0.2537173 0.6880155 0.02269657 0.03955622 0.013403304
## 154 154 0.8649964 0.2538537 0.6880612 0.02260950 0.03944471 0.013421036
## 155 155 0.8650821 0.2537309 0.6880968 0.02265340 0.03940534 0.013432579
## 156 156 0.8649077 0.2540062 0.6879445 0.02263485 0.03944523 0.013401900
## 157 157 0.8649641 0.2539305 0.6880155 0.02262869 0.03969687 0.013520994
## 158 158 0.8649234 0.2539996 0.6878611 0.02274447 0.03980981 0.013575140
## 159 159 0.8650259 0.2538343 0.6879691 0.02279695 0.03966635 0.013542512
## 160 160 0.8651160 0.2536991 0.6880874 0.02285787 0.03974293 0.013594024
## 161 161 0.8650798 0.2537488 0.6879484 0.02287713 0.03961311 0.013614135
## 162 162 0.8650008 0.2538785 0.6879524 0.02288637 0.03973423 0.013515565
## 163 163 0.8650229 0.2538278 0.6878366 0.02288527 0.03954187 0.013458375
## 164 164 0.8649360 0.2539715 0.6877503 0.02286533 0.03956482 0.013475827
## 165 165 0.8649814 0.2539148 0.6878781 0.02288570 0.03954622 0.013432730
## 166 166 0.8649533 0.2539656 0.6878676 0.02291764 0.03956857 0.013405414
## 167 167 0.8649512 0.2539596 0.6877757 0.02283213 0.03951182 0.013335826
## 168 168 0.8649096 0.2540279 0.6877129 0.02275777 0.03940992 0.013285570
## 169 169 0.8649216 0.2540138 0.6876275 0.02259689 0.03940282 0.013223015
## 170 170 0.8649207 0.2540096 0.6876627 0.02263433 0.03955539 0.013221490
## 171 171 0.8649235 0.2540171 0.6875885 0.02258740 0.03965776 0.013187631
## 172 172 0.8649508 0.2539780 0.6876411 0.02254543 0.03970452 0.013197996
## 173 173 0.8649970 0.2538890 0.6876665 0.02237715 0.03954359 0.013021291
## 174 174 0.8648836 0.2540468 0.6875681 0.02232413 0.03946183 0.012925155
## 175 175 0.8648282 0.2541226 0.6875299 0.02228718 0.03933844 0.012800913
## 176 176 0.8647997 0.2541714 0.6875225 0.02241677 0.03941261 0.012910364
## 177 177 0.8647839 0.2542032 0.6874771 0.02235304 0.03954720 0.012816806
## 178 178 0.8648280 0.2541248 0.6874281 0.02234789 0.03943570 0.012811567
## 179 179 0.8647099 0.2543013 0.6873546 0.02229518 0.03938820 0.012776900
## 180 180 0.8647264 0.2542661 0.6873968 0.02228665 0.03928715 0.012732770
## 181 181 0.8646815 0.2543407 0.6873848 0.02228805 0.03933245 0.012725031
## 182 182 0.8646041 0.2544558 0.6873119 0.02230144 0.03934633 0.012697990
## 183 183 0.8646519 0.2543940 0.6873949 0.02230390 0.03936280 0.012712955
## 184 184 0.8645620 0.2545317 0.6873521 0.02228590 0.03932497 0.012671304
## 185 185 0.8645212 0.2545974 0.6873054 0.02226618 0.03936712 0.012727295
## 186 186 0.8645383 0.2545726 0.6873188 0.02237588 0.03949010 0.012825288
## 187 187 0.8645223 0.2545981 0.6873238 0.02234586 0.03942640 0.012824781
## 188 188 0.8644745 0.2546631 0.6872872 0.02235213 0.03932737 0.012835613
## 189 189 0.8645203 0.2546023 0.6872930 0.02242791 0.03935677 0.012873482
## 190 190 0.8645173 0.2546063 0.6873051 0.02239633 0.03925129 0.012846322
## 191 191 0.8644846 0.2546522 0.6872394 0.02244408 0.03920962 0.012874803
## 192 192 0.8644630 0.2546868 0.6872773 0.02241446 0.03921346 0.012851170
## 193 193 0.8644793 0.2546620 0.6872566 0.02249505 0.03924786 0.012886347
## 194 194 0.8643716 0.2548227 0.6871672 0.02260590 0.03920802 0.012994150
## 195 195 0.8643247 0.2549015 0.6871332 0.02267170 0.03927658 0.013057683
## 196 196 0.8643249 0.2549101 0.6871602 0.02264013 0.03930017 0.013028661
## 197 197 0.8643066 0.2549354 0.6871575 0.02263254 0.03935453 0.012971612
## 198 198 0.8643485 0.2548766 0.6871632 0.02260358 0.03934294 0.012984026
## 199 199 0.8644304 0.2547509 0.6872445 0.02264901 0.03930697 0.012997163
## 200 200 0.8644906 0.2546633 0.6872835 0.02267099 0.03928964 0.012979727
## 201 201 0.8644894 0.2546759 0.6872934 0.02272327 0.03938297 0.013020955
## 202 202 0.8645021 0.2546602 0.6873088 0.02271924 0.03939428 0.013039244
## 203 203 0.8644821 0.2546887 0.6872942 0.02269680 0.03938579 0.013027852
## 204 204 0.8644431 0.2547443 0.6872819 0.02269181 0.03937080 0.013009381
## 205 205 0.8643942 0.2548163 0.6872425 0.02268286 0.03937469 0.013042317
## 206 206 0.8644213 0.2547662 0.6872682 0.02270022 0.03936765 0.013057544
## 207 207 0.8644149 0.2547746 0.6872744 0.02264557 0.03931264 0.013054076
## 208 208 0.8643613 0.2548551 0.6872490 0.02271526 0.03937942 0.013071076
## 209 209 0.8643789 0.2548285 0.6872292 0.02267950 0.03937777 0.013058965
## 210 210 0.8644623 0.2546983 0.6873041 0.02266541 0.03939790 0.013031885
## 211 211 0.8644711 0.2546796 0.6872596 0.02260680 0.03933482 0.013000818
## 212 212 0.8644406 0.2547198 0.6872810 0.02253974 0.03924487 0.012967461
## 213 213 0.8644408 0.2547157 0.6872829 0.02255598 0.03921702 0.012971348
## 214 214 0.8644547 0.2546908 0.6872942 0.02254064 0.03915361 0.012995556
## 215 215 0.8644686 0.2546762 0.6873124 0.02253897 0.03916825 0.013026854
## 216 216 0.8644366 0.2547242 0.6873047 0.02250242 0.03910234 0.013004448
## 217 217 0.8643800 0.2548140 0.6872594 0.02254826 0.03915010 0.013054589
## 218 218 0.8643650 0.2548346 0.6872318 0.02253205 0.03914649 0.013068420
## 219 219 0.8643674 0.2548320 0.6872374 0.02249098 0.03911001 0.013042970
## 220 220 0.8643969 0.2547848 0.6872646 0.02247126 0.03908679 0.013036641
## 221 221 0.8644215 0.2547497 0.6872928 0.02250089 0.03909540 0.013039164
## 222 222 0.8644478 0.2547095 0.6873248 0.02252380 0.03909233 0.013045194
## 223 223 0.8644213 0.2547491 0.6873061 0.02250426 0.03907270 0.013040315
## 224 224 0.8644182 0.2547531 0.6873016 0.02250105 0.03906381 0.013041798
## 225 225 0.8644054 0.2547735 0.6872896 0.02249155 0.03904425 0.013034848
## 226 226 0.8644061 0.2547710 0.6872857 0.02252556 0.03906664 0.013050885
## 227 227 0.8644351 0.2547275 0.6873143 0.02253630 0.03907459 0.013069680
## 228 228 0.8644320 0.2547320 0.6873170 0.02253255 0.03907449 0.013064895
## 229 229 0.8644077 0.2547698 0.6872986 0.02253796 0.03908791 0.013070851
## 230 230 0.8644094 0.2547651 0.6873059 0.02254183 0.03909273 0.013078633
## 231 231 0.8644020 0.2547747 0.6872930 0.02253830 0.03909598 0.013083967
## 232 232 0.8644039 0.2547722 0.6873020 0.02253601 0.03909419 0.013077635
## 233 233 0.8644071 0.2547669 0.6873097 0.02253836 0.03910302 0.013080915
## 234 234 0.8643971 0.2547811 0.6873031 0.02253398 0.03908905 0.013073840
## 235 235 0.8643967 0.2547820 0.6873027 0.02253445 0.03909576 0.013074208
## 236 236 0.8644035 0.2547713 0.6873088 0.02253801 0.03910281 0.013074828
## 237 237 0.8644062 0.2547673 0.6873107 0.02253661 0.03909711 0.013068239
## 238 238 0.8644052 0.2547685 0.6873100 0.02253263 0.03909217 0.013064110
## 239 239 0.8644026 0.2547719 0.6873081 0.02253208 0.03909270 0.013066488
## 240 240 0.8644000 0.2547759 0.6873056 0.02253218 0.03909530 0.013066612
## nvmax
## 11 11
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Coefficients of final model:
## (Intercept) x4 x7 x9 x10
## -3.107249329 -0.001286918 0.338846892 0.098579372 0.037105764
## x16 x17 x21 stat14 stat98
## 0.026656795 0.033446343 0.004230119 -0.023583076 0.103757551
## stat110 sqrt.x18
## -0.094831072 0.800160707Test
if (algo.forward.caret == TRUE){
test.model(model.forward, data.test
,method = 'leapForward',subopt = NULL
,formula = formula, feature.names = feature.names, label.names = label.names
,id = id
,draw.limits = TRUE, transformation = t)
}## [1] "Summary of predicted values: "
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -1.73261 -0.36478 -0.01048 -0.01629 0.36378 1.30380
## [1] "leapForward Test MSE: 0.747502177162688"
Forward Selection with CV (w/ filtered train)
Train
if (algo.forward.caret == TRUE){
set.seed(1)
returned = train.caret.glmselect(formula = formula
,data = data.train2
,method = "leapForward"
,feature.names = feature.names)
model.forward = returned$model
id = returned$id
}## Aggregating results
## Selecting tuning parameters
## Fitting nvmax = 16 on full training set
## nvmax RMSE Rsquared MAE RMSESD RsquaredSD MAESD
## 1 1 0.8324519 0.1749919 0.6790566 0.01597113 0.03921811 0.01317663
## 2 2 0.7975540 0.2419521 0.6557232 0.01520566 0.03509192 0.01335539
## 3 3 0.7750513 0.2839167 0.6353240 0.01931842 0.02940618 0.01851574
## 4 4 0.7546417 0.3209165 0.6150965 0.01930866 0.02741817 0.01668461
## 5 5 0.7432158 0.3417524 0.6063017 0.02162484 0.02238676 0.01910499
## 6 6 0.7401442 0.3470278 0.6037990 0.02181541 0.02075352 0.02001697
## 7 7 0.7392300 0.3484991 0.6043955 0.01983376 0.01849274 0.01875508
## 8 8 0.7383514 0.3499971 0.6041595 0.01935401 0.01880114 0.01779459
## 9 9 0.7359585 0.3540996 0.6023114 0.01880218 0.01718949 0.01713525
## 10 10 0.7340769 0.3573473 0.6008371 0.01896156 0.01735750 0.01725500
## 11 11 0.7323836 0.3603312 0.5991209 0.01940450 0.01799580 0.01818901
## 12 12 0.7333335 0.3587364 0.6004735 0.01960659 0.01699529 0.01835213
## 13 13 0.7332249 0.3589732 0.6005063 0.01986340 0.01808885 0.01849686
## 14 14 0.7326772 0.3599495 0.5999679 0.02020355 0.01789635 0.01880456
## 15 15 0.7323875 0.3604614 0.5998161 0.01923768 0.01777084 0.01789792
## 16 16 0.7317308 0.3616345 0.5988380 0.02011027 0.01783930 0.01840313
## 17 17 0.7320915 0.3610218 0.5990603 0.02023952 0.01752253 0.01851289
## 18 18 0.7323446 0.3606723 0.5993921 0.02074202 0.01698115 0.01909585
## 19 19 0.7325729 0.3603103 0.5995546 0.02099447 0.01600432 0.01952815
## 20 20 0.7326691 0.3601558 0.5999234 0.02138390 0.01537972 0.02016294
## 21 21 0.7328240 0.3599509 0.6004806 0.02207385 0.01629356 0.02064490
## 22 22 0.7330182 0.3596177 0.6007959 0.02166374 0.01679292 0.02015044
## 23 23 0.7332598 0.3592186 0.6013791 0.02092943 0.01664991 0.01999957
## 24 24 0.7332448 0.3592183 0.6009740 0.02063897 0.01618363 0.01992244
## 25 25 0.7342082 0.3575969 0.6016262 0.02029283 0.01558627 0.02018328
## 26 26 0.7345005 0.3571599 0.6016931 0.02097337 0.01611577 0.02074803
## 27 27 0.7346711 0.3569035 0.6017088 0.02167007 0.01742785 0.02091274
## 28 28 0.7345268 0.3571791 0.6017894 0.02157050 0.01679695 0.02088523
## 29 29 0.7349105 0.3565451 0.6020673 0.02148822 0.01646155 0.02067036
## 30 30 0.7348447 0.3566504 0.6019782 0.02138730 0.01727672 0.02044088
## 31 31 0.7349366 0.3565394 0.6019002 0.02136110 0.01833522 0.02055157
## 32 32 0.7356447 0.3553671 0.6023556 0.02158618 0.01891122 0.02097806
## 33 33 0.7363321 0.3542503 0.6031065 0.02181796 0.01941209 0.02089048
## 34 34 0.7365924 0.3538671 0.6032072 0.02190434 0.01906906 0.02127215
## 35 35 0.7370145 0.3531759 0.6034955 0.02226525 0.01973269 0.02140380
## 36 36 0.7373133 0.3526752 0.6037595 0.02219370 0.02065592 0.02101076
## 37 37 0.7376039 0.3522015 0.6038814 0.02213294 0.02060919 0.02106023
## 38 38 0.7379776 0.3516109 0.6044713 0.02223735 0.02100682 0.02126415
## 39 39 0.7379024 0.3517842 0.6043139 0.02242406 0.02094488 0.02116351
## 40 40 0.7378765 0.3518389 0.6043357 0.02243091 0.02103150 0.02095125
## 41 41 0.7376665 0.3521774 0.6042960 0.02233423 0.02142788 0.02101720
## 42 42 0.7379881 0.3516769 0.6046059 0.02240736 0.02108816 0.02096528
## 43 43 0.7378361 0.3519534 0.6042324 0.02226680 0.02074679 0.02090386
## 44 44 0.7382872 0.3512384 0.6041947 0.02189289 0.02045952 0.02068742
## 45 45 0.7382877 0.3512769 0.6041154 0.02139786 0.02034765 0.02022171
## 46 46 0.7385854 0.3508137 0.6042597 0.02134150 0.01952527 0.02000914
## 47 47 0.7385849 0.3508801 0.6043591 0.02120283 0.01977125 0.01984872
## 48 48 0.7389071 0.3503609 0.6044822 0.02139428 0.02032603 0.02013375
## 49 49 0.7389951 0.3503045 0.6046331 0.02191091 0.02059764 0.02047063
## 50 50 0.7393215 0.3497661 0.6050588 0.02130023 0.01989800 0.01994521
## 51 51 0.7393206 0.3498023 0.6049312 0.02114386 0.02022078 0.01975642
## 52 52 0.7399174 0.3488209 0.6055595 0.02124910 0.02038553 0.01992707
## 53 53 0.7403436 0.3481117 0.6060363 0.02167736 0.02065018 0.02033069
## 54 54 0.7403811 0.3480504 0.6058468 0.02191151 0.02103714 0.02046613
## 55 55 0.7404286 0.3480323 0.6060130 0.02218750 0.02128079 0.02060629
## 56 56 0.7402238 0.3483974 0.6059455 0.02218637 0.02146898 0.02081889
## 57 57 0.7400539 0.3486711 0.6059235 0.02190785 0.02145685 0.02051266
## 58 58 0.7398769 0.3490319 0.6058627 0.02235558 0.02194019 0.02095252
## 59 59 0.7397353 0.3492628 0.6056259 0.02212074 0.02155289 0.02076264
## 60 60 0.7396736 0.3493986 0.6054122 0.02220915 0.02156932 0.02084740
## 61 61 0.7394201 0.3498369 0.6052025 0.02253277 0.02218267 0.02083848
## 62 62 0.7391725 0.3502532 0.6049783 0.02239568 0.02200648 0.02058027
## 63 63 0.7391922 0.3502756 0.6048641 0.02265072 0.02153243 0.02085263
## 64 64 0.7389413 0.3506903 0.6047627 0.02252729 0.02199284 0.02073890
## 65 65 0.7389368 0.3506956 0.6046038 0.02219214 0.02208828 0.02055256
## 66 66 0.7386319 0.3511813 0.6043738 0.02149339 0.02199982 0.02016029
## 67 67 0.7383130 0.3517112 0.6041663 0.02118363 0.02183104 0.01990274
## 68 68 0.7380514 0.3521995 0.6041422 0.02138648 0.02121764 0.01996477
## 69 69 0.7380494 0.3522645 0.6040087 0.02133296 0.02160390 0.01999324
## 70 70 0.7379551 0.3524086 0.6038579 0.02122063 0.02175258 0.02009701
## 71 71 0.7378928 0.3524884 0.6036393 0.02047379 0.02162333 0.01965901
## 72 72 0.7378624 0.3525370 0.6035342 0.02039187 0.02140687 0.01952176
## 73 73 0.7378093 0.3525992 0.6035538 0.02039767 0.02209768 0.01929996
## 74 74 0.7376282 0.3529033 0.6033573 0.02015184 0.02194175 0.01908782
## 75 75 0.7375426 0.3530648 0.6034570 0.02032810 0.02126425 0.01920855
## 76 76 0.7377824 0.3527337 0.6034535 0.02078903 0.02141399 0.01973693
## 77 77 0.7378175 0.3527182 0.6035429 0.02085089 0.02130506 0.01990648
## 78 78 0.7371154 0.3539356 0.6030241 0.02099765 0.02092174 0.02038348
## 79 79 0.7373815 0.3535069 0.6033969 0.02059145 0.02032147 0.02016546
## 80 80 0.7374784 0.3533709 0.6035277 0.02048341 0.01992522 0.02025459
## 81 81 0.7371774 0.3538675 0.6033354 0.02100291 0.01972934 0.02059331
## 82 82 0.7368976 0.3543310 0.6032319 0.02074117 0.01923164 0.02044822
## 83 83 0.7368574 0.3544208 0.6032871 0.02075510 0.01891356 0.02046862
## 84 84 0.7363434 0.3552802 0.6027886 0.02122263 0.01930144 0.02100892
## 85 85 0.7362222 0.3554761 0.6026454 0.02118433 0.01975910 0.02103862
## 86 86 0.7362577 0.3554368 0.6027265 0.02090414 0.01947319 0.02086883
## 87 87 0.7360706 0.3557495 0.6024844 0.02093881 0.01972327 0.02076017
## 88 88 0.7360699 0.3557367 0.6023860 0.02081388 0.01969525 0.02062945
## 89 89 0.7357586 0.3562447 0.6020572 0.02051169 0.01970748 0.02045193
## 90 90 0.7357832 0.3562374 0.6020626 0.02001360 0.01955427 0.02001575
## 91 91 0.7356592 0.3564809 0.6018401 0.02012821 0.02001343 0.02006998
## 92 92 0.7358895 0.3561033 0.6019516 0.02015192 0.02025071 0.01999181
## 93 93 0.7358646 0.3561385 0.6017824 0.02003264 0.02072129 0.01977868
## 94 94 0.7356166 0.3566071 0.6016653 0.02033719 0.02084515 0.02021740
## 95 95 0.7353937 0.3569574 0.6015695 0.02059874 0.02138268 0.02048761
## 96 96 0.7354868 0.3568502 0.6016436 0.02058054 0.02110329 0.02036123
## 97 97 0.7356433 0.3566011 0.6017703 0.02090539 0.02155907 0.02070889
## 98 98 0.7355183 0.3568036 0.6016402 0.02090088 0.02158139 0.02078257
## 99 99 0.7354074 0.3569920 0.6017196 0.02081912 0.02163129 0.02069336
## 100 100 0.7353140 0.3571618 0.6016336 0.02086296 0.02179981 0.02070703
## 101 101 0.7352776 0.3572285 0.6014092 0.02075862 0.02168041 0.02044485
## 102 102 0.7352650 0.3572443 0.6013765 0.02078239 0.02201892 0.02041568
## 103 103 0.7354640 0.3569062 0.6015817 0.02069117 0.02225740 0.02026260
## 104 104 0.7354482 0.3569571 0.6015567 0.02062036 0.02200800 0.02031814
## 105 105 0.7355662 0.3568108 0.6017786 0.02060204 0.02195803 0.02022892
## 106 106 0.7354659 0.3570051 0.6016554 0.02076179 0.02244106 0.02044068
## 107 107 0.7353010 0.3573178 0.6015818 0.02075902 0.02231064 0.02053614
## 108 108 0.7352984 0.3573370 0.6017411 0.02051011 0.02194164 0.02042186
## 109 109 0.7356622 0.3567658 0.6021582 0.02041596 0.02206607 0.02031842
## 110 110 0.7353081 0.3573685 0.6018944 0.02038624 0.02220566 0.02021478
## 111 111 0.7352944 0.3573787 0.6020321 0.02042582 0.02246222 0.02032799
## 112 112 0.7349242 0.3580283 0.6017072 0.02044619 0.02212015 0.02027162
## 113 113 0.7350185 0.3578851 0.6017384 0.02033850 0.02232531 0.02008932
## 114 114 0.7349472 0.3580173 0.6016314 0.02024842 0.02237857 0.02006515
## 115 115 0.7347173 0.3583929 0.6016667 0.02024741 0.02282081 0.01997510
## 116 116 0.7346328 0.3585174 0.6016682 0.02000160 0.02264708 0.01969337
## 117 117 0.7347522 0.3582998 0.6019423 0.01994215 0.02293843 0.01950985
## 118 118 0.7347545 0.3583085 0.6018902 0.02000234 0.02325583 0.01948156
## 119 119 0.7348738 0.3581315 0.6020717 0.01996431 0.02279336 0.01946380
## 120 120 0.7349805 0.3579986 0.6022664 0.02007030 0.02292287 0.01957122
## 121 121 0.7350066 0.3579819 0.6022706 0.02043212 0.02306808 0.01973628
## 122 122 0.7349626 0.3580675 0.6022036 0.02056818 0.02350024 0.01985874
## 123 123 0.7346922 0.3585011 0.6020041 0.02042951 0.02330534 0.01979275
## 124 124 0.7348491 0.3582794 0.6020624 0.02042300 0.02334373 0.01965857
## 125 125 0.7347121 0.3585228 0.6019015 0.02056992 0.02317579 0.01977781
## 126 126 0.7346797 0.3585820 0.6019018 0.02057974 0.02334711 0.01974505
## 127 127 0.7346852 0.3585881 0.6018746 0.02047302 0.02304804 0.01970247
## 128 128 0.7346112 0.3587215 0.6019498 0.02063170 0.02308819 0.01991203
## 129 129 0.7348070 0.3584207 0.6020158 0.02088588 0.02353142 0.02011189
## 130 130 0.7347810 0.3584657 0.6019700 0.02092242 0.02313926 0.02016210
## 131 131 0.7348609 0.3583514 0.6021615 0.02059272 0.02306314 0.01978911
## 132 132 0.7349062 0.3582977 0.6023246 0.02073440 0.02328970 0.01982451
## 133 133 0.7350584 0.3580742 0.6024091 0.02086043 0.02299783 0.01999944
## 134 134 0.7350120 0.3581685 0.6023273 0.02094342 0.02274809 0.02000645
## 135 135 0.7349491 0.3582852 0.6023449 0.02092144 0.02257498 0.01998966
## 136 136 0.7349746 0.3582276 0.6023405 0.02086354 0.02262026 0.01987871
## 137 137 0.7349561 0.3582447 0.6022442 0.02066727 0.02267322 0.01971599
## 138 138 0.7350721 0.3580490 0.6024058 0.02057839 0.02254218 0.01969842
## 139 139 0.7352386 0.3578113 0.6025343 0.02077906 0.02262908 0.01996001
## 140 140 0.7352993 0.3577176 0.6025495 0.02070915 0.02279222 0.01988342
## 141 141 0.7352289 0.3578178 0.6023970 0.02066793 0.02261960 0.01967742
## 142 142 0.7352066 0.3578469 0.6024744 0.02050622 0.02251380 0.01947901
## 143 143 0.7352732 0.3577406 0.6025236 0.02052201 0.02275018 0.01950879
## 144 144 0.7352550 0.3577715 0.6024521 0.02047916 0.02256530 0.01950721
## 145 145 0.7353052 0.3576993 0.6024445 0.02049665 0.02270834 0.01953909
## 146 146 0.7352259 0.3578451 0.6024492 0.02044119 0.02289901 0.01947082
## 147 147 0.7353261 0.3576950 0.6025276 0.02039442 0.02289574 0.01938445
## 148 148 0.7351867 0.3579305 0.6023213 0.02027249 0.02282724 0.01922273
## 149 149 0.7352948 0.3577514 0.6024192 0.02027836 0.02293906 0.01931777
## 150 150 0.7353986 0.3576005 0.6023673 0.02030996 0.02280965 0.01925793
## 151 151 0.7354945 0.3574567 0.6024645 0.02041364 0.02274089 0.01935962
## 152 152 0.7355798 0.3573101 0.6025882 0.02044953 0.02259879 0.01942965
## 153 153 0.7356187 0.3572479 0.6026219 0.02031608 0.02245615 0.01944229
## 154 154 0.7355654 0.3573327 0.6027001 0.02027513 0.02226130 0.01932827
## 155 155 0.7356223 0.3572536 0.6027456 0.02029502 0.02215360 0.01929469
## 156 156 0.7355270 0.3573992 0.6026085 0.02031538 0.02239131 0.01930168
## 157 157 0.7356555 0.3571859 0.6027830 0.02025472 0.02235577 0.01918353
## 158 158 0.7356604 0.3571624 0.6028001 0.02009712 0.02245058 0.01909470
## 159 159 0.7357345 0.3570617 0.6028412 0.02012363 0.02237033 0.01919861
## 160 160 0.7357502 0.3570406 0.6028558 0.02024849 0.02217818 0.01928138
## 161 161 0.7357689 0.3570035 0.6028440 0.02030917 0.02230695 0.01928129
## 162 162 0.7358427 0.3568780 0.6029562 0.02033418 0.02242280 0.01936560
## 163 163 0.7359036 0.3567691 0.6029968 0.02019872 0.02238022 0.01924739
## 164 164 0.7358423 0.3568758 0.6029833 0.02016818 0.02230711 0.01921919
## 165 165 0.7358908 0.3568134 0.6030876 0.02019595 0.02248488 0.01919782
## 166 166 0.7359917 0.3566555 0.6031968 0.02025136 0.02256746 0.01926947
## 167 167 0.7360593 0.3565508 0.6032875 0.02024253 0.02285545 0.01928371
## 168 168 0.7360708 0.3565264 0.6032528 0.02029177 0.02307711 0.01923550
## 169 169 0.7360986 0.3564911 0.6032459 0.02029108 0.02282245 0.01919061
## 170 170 0.7361066 0.3564997 0.6032139 0.02051943 0.02280915 0.01935794
## 171 171 0.7361922 0.3563761 0.6032875 0.02053099 0.02270030 0.01941508
## 172 172 0.7361870 0.3563913 0.6033291 0.02054724 0.02275529 0.01944450
## 173 173 0.7360904 0.3565661 0.6032021 0.02064072 0.02314241 0.01954299
## 174 174 0.7361330 0.3564984 0.6032247 0.02072602 0.02326668 0.01964085
## 175 175 0.7360955 0.3565531 0.6031934 0.02068851 0.02328494 0.01962552
## 176 176 0.7361005 0.3565387 0.6032086 0.02058633 0.02310954 0.01955443
## 177 177 0.7360489 0.3566334 0.6032137 0.02068280 0.02304224 0.01964055
## 178 178 0.7359967 0.3567102 0.6031952 0.02071186 0.02305145 0.01973485
## 179 179 0.7360192 0.3566840 0.6032226 0.02076362 0.02295494 0.01981959
## 180 180 0.7361559 0.3564727 0.6033416 0.02074897 0.02294859 0.01982623
## 181 181 0.7361122 0.3565599 0.6033256 0.02074892 0.02293681 0.01979216
## 182 182 0.7360568 0.3566488 0.6032498 0.02080545 0.02286649 0.01983461
## 183 183 0.7360444 0.3566669 0.6032471 0.02079132 0.02270318 0.01980596
## 184 184 0.7361034 0.3565600 0.6033651 0.02083853 0.02272547 0.01982043
## 185 185 0.7361274 0.3565112 0.6033983 0.02077842 0.02282283 0.01981273
## 186 186 0.7361821 0.3564216 0.6034148 0.02078699 0.02279464 0.01980105
## 187 187 0.7361713 0.3564377 0.6033649 0.02077184 0.02292662 0.01976069
## 188 188 0.7362432 0.3563242 0.6033731 0.02082400 0.02295372 0.01982628
## 189 189 0.7362216 0.3563726 0.6033070 0.02087841 0.02301399 0.01989373
## 190 190 0.7362321 0.3563631 0.6032299 0.02096686 0.02309367 0.01994194
## 191 191 0.7362722 0.3562957 0.6032655 0.02103327 0.02302426 0.01996181
## 192 192 0.7363071 0.3562309 0.6033130 0.02096546 0.02311303 0.01986608
## 193 193 0.7362870 0.3562733 0.6032595 0.02097364 0.02298052 0.01983520
## 194 194 0.7361930 0.3564260 0.6031829 0.02086383 0.02295529 0.01973800
## 195 195 0.7362752 0.3563110 0.6031998 0.02089017 0.02287094 0.01978842
## 196 196 0.7362508 0.3563532 0.6032242 0.02087675 0.02283725 0.01972473
## 197 197 0.7362555 0.3563449 0.6032399 0.02078956 0.02277090 0.01966612
## 198 198 0.7362122 0.3564133 0.6032047 0.02079698 0.02285234 0.01969064
## 199 199 0.7362243 0.3563833 0.6032754 0.02076058 0.02288276 0.01970422
## 200 200 0.7362664 0.3563188 0.6033132 0.02078006 0.02290797 0.01965766
## 201 201 0.7362543 0.3563388 0.6032910 0.02075840 0.02296224 0.01965497
## 202 202 0.7361850 0.3564554 0.6032540 0.02079669 0.02298993 0.01966093
## 203 203 0.7361859 0.3564578 0.6032621 0.02079045 0.02302413 0.01962379
## 204 204 0.7361707 0.3564813 0.6032381 0.02081960 0.02312906 0.01963348
## 205 205 0.7362553 0.3563383 0.6033462 0.02076036 0.02301861 0.01958192
## 206 206 0.7362343 0.3563661 0.6033538 0.02069930 0.02309719 0.01953676
## 207 207 0.7362683 0.3563106 0.6033954 0.02074759 0.02319062 0.01958893
## 208 208 0.7362996 0.3562583 0.6034578 0.02073907 0.02320238 0.01959917
## 209 209 0.7363463 0.3561876 0.6034936 0.02070905 0.02313148 0.01954959
## 210 210 0.7363528 0.3561767 0.6034855 0.02069973 0.02309986 0.01956347
## 211 211 0.7363731 0.3561480 0.6034945 0.02069744 0.02313577 0.01957390
## 212 212 0.7364060 0.3560966 0.6035242 0.02071042 0.02316844 0.01959291
## 213 213 0.7364278 0.3560595 0.6035628 0.02074890 0.02319792 0.01961407
## 214 214 0.7364332 0.3560466 0.6035766 0.02076227 0.02323864 0.01962494
## 215 215 0.7364374 0.3560361 0.6035747 0.02077239 0.02326901 0.01964388
## 216 216 0.7364224 0.3560600 0.6035538 0.02078105 0.02331385 0.01965073
## 217 217 0.7364617 0.3559932 0.6035627 0.02076519 0.02322354 0.01964862
## 218 218 0.7364361 0.3560375 0.6035308 0.02075059 0.02327440 0.01963288
## 219 219 0.7364998 0.3559364 0.6035751 0.02073213 0.02328795 0.01963967
## 220 220 0.7364683 0.3559884 0.6035379 0.02073851 0.02327453 0.01966850
## 221 221 0.7364776 0.3559707 0.6035528 0.02073487 0.02329445 0.01966415
## 222 222 0.7364621 0.3559976 0.6035377 0.02073244 0.02330522 0.01968611
## 223 223 0.7364648 0.3559927 0.6035461 0.02071560 0.02329293 0.01966586
## 224 224 0.7364598 0.3560022 0.6035254 0.02074556 0.02333190 0.01970377
## 225 225 0.7364701 0.3559830 0.6035310 0.02076814 0.02332419 0.01972144
## 226 226 0.7364904 0.3559525 0.6035233 0.02080854 0.02334562 0.01974655
## 227 227 0.7364982 0.3559344 0.6035327 0.02081637 0.02329900 0.01976604
## 228 228 0.7365073 0.3559170 0.6035446 0.02078040 0.02326653 0.01973810
## 229 229 0.7365224 0.3558955 0.6035628 0.02079335 0.02323583 0.01974396
## 230 230 0.7365405 0.3558696 0.6035757 0.02079506 0.02324264 0.01976618
## 231 231 0.7365367 0.3558742 0.6035684 0.02078369 0.02321248 0.01976573
## 232 232 0.7365415 0.3558677 0.6035674 0.02079166 0.02321487 0.01976002
## 233 233 0.7365500 0.3558528 0.6035691 0.02078198 0.02320991 0.01974132
## 234 234 0.7365563 0.3558445 0.6035734 0.02079531 0.02323921 0.01974396
## 235 235 0.7365678 0.3558236 0.6035820 0.02079426 0.02323339 0.01974909
## 236 236 0.7365770 0.3558086 0.6035879 0.02078777 0.02325910 0.01975093
## 237 237 0.7365706 0.3558182 0.6035817 0.02078349 0.02325596 0.01974991
## 238 238 0.7365684 0.3558213 0.6035815 0.02078640 0.02326370 0.01975398
## 239 239 0.7365711 0.3558170 0.6035828 0.02078492 0.02325991 0.01975210
## 240 240 0.7365714 0.3558164 0.6035841 0.02078390 0.02325952 0.01975040
## nvmax
## 16 16
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Coefficients of final model:
## (Intercept) x4 x7 x8 x9
## -3.492157240 -0.001454545 0.355484220 0.012272806 0.101739706
## x10 x13 x16 x17 x21
## 0.043243708 0.006184991 0.024719698 0.035827337 0.004385325
## stat4 stat14 stat41 stat98 stat110
## -0.020060046 -0.029429292 -0.018983255 0.108150933 -0.100901219
## stat144 sqrt.x18
## 0.017815602 0.834483878Test
if (algo.forward.caret == TRUE){
test.model(model.forward, data.test
,method = 'leapForward',subopt = NULL
,formula = formula, feature.names = feature.names, label.names = label.names
,id = id
,draw.limits = TRUE, transformation = t)
}## [1] "Summary of predicted values: "
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -1.87585 -0.43536 -0.06651 -0.07052 0.31776 1.30819
## [1] "leapForward Test MSE: 0.7529414468572"
Backward Elimination
Train
if (algo.backward == TRUE){
# Takes too much time
t1 = Sys.time()
model.backward = step(model.full, data = data.train, direction="backward", trace = 0)
print(summary(model.backward))
t2 = Sys.time()
print (paste("Time taken for Backward Elimination: ",t2-t1, sep = ""))
plot.diagnostics(model.backward, data.train)
}Test
if (algo.backward == TRUE){
test.model(model.backard, data.test, "Backward Elimination")
}Backward Elimination with CV (w/ full train)
Train
if (algo.backward.caret == TRUE){
set.seed(1)
returned = train.caret.glmselect(formula = formula
,data = data.train
,method = "leapBackward"
,feature.names = feature.names)
model.backward = returned$model
id = returned$id
}## Aggregating results
## Selecting tuning parameters
## Fitting nvmax = 11 on full training set
## nvmax RMSE Rsquared MAE RMSESD RsquaredSD MAESD
## 1 1 0.9290962 0.1383305 0.7452032 0.02166207 0.04198587 0.014135754
## 2 2 0.9013733 0.1870866 0.7246099 0.01861952 0.03823400 0.010184999
## 3 3 0.8828368 0.2193324 0.7063992 0.01966356 0.03628172 0.013394655
## 4 4 0.8674643 0.2460619 0.6887616 0.01704415 0.03510583 0.008406016
## 5 5 0.8579880 0.2627745 0.6814284 0.02179327 0.04052670 0.012261767
## 6 6 0.8569684 0.2645498 0.6802092 0.02203645 0.04140563 0.012736876
## 7 7 0.8563457 0.2657719 0.6808046 0.02394403 0.04272918 0.013719134
## 8 8 0.8534905 0.2706668 0.6783829 0.02287015 0.04290888 0.012749598
## 9 9 0.8528383 0.2717334 0.6776977 0.02335663 0.04296204 0.013431847
## 10 10 0.8526548 0.2720586 0.6779706 0.02376427 0.04267483 0.013589627
## 11 11 0.8508870 0.2750101 0.6760872 0.02343404 0.04253772 0.013138789
## 12 12 0.8511750 0.2745439 0.6765241 0.02288446 0.04185114 0.012393757
## 13 13 0.8517134 0.2736653 0.6773621 0.02284295 0.04132396 0.012437282
## 14 14 0.8518478 0.2734689 0.6775363 0.02308257 0.04175450 0.012522662
## 15 15 0.8523132 0.2726861 0.6785336 0.02287065 0.04124222 0.012314134
## 16 16 0.8526225 0.2721730 0.6788055 0.02222458 0.04043358 0.011840572
## 17 17 0.8528898 0.2718155 0.6789886 0.02260654 0.04194517 0.012365083
## 18 18 0.8540215 0.2699360 0.6799038 0.02283415 0.04257539 0.013054530
## 19 19 0.8541910 0.2696181 0.6801496 0.02289056 0.04211078 0.012831512
## 20 20 0.8541721 0.2696737 0.6801220 0.02305524 0.04295958 0.012883581
## 21 21 0.8540377 0.2699964 0.6800000 0.02340748 0.04278618 0.013013284
## 22 22 0.8537160 0.2705355 0.6801630 0.02327103 0.04243395 0.013064691
## 23 23 0.8542592 0.2696194 0.6805248 0.02337080 0.04281720 0.013289804
## 24 24 0.8542880 0.2695184 0.6802718 0.02289806 0.04206660 0.012672175
## 25 25 0.8544619 0.2691764 0.6803247 0.02279861 0.04208416 0.012217146
## 26 26 0.8548445 0.2685096 0.6804798 0.02274286 0.04117687 0.012264468
## 27 27 0.8548010 0.2686363 0.6803991 0.02285009 0.04157289 0.012456412
## 28 28 0.8551304 0.2680779 0.6806202 0.02205566 0.04063723 0.011831738
## 29 29 0.8547024 0.2687965 0.6800072 0.02202766 0.04040219 0.011772600
## 30 30 0.8549589 0.2684074 0.6803856 0.02202922 0.03989320 0.011639163
## 31 31 0.8546342 0.2689882 0.6799935 0.02185273 0.03967867 0.011385428
## 32 32 0.8547427 0.2688249 0.6797585 0.02218458 0.03969216 0.011389108
## 33 33 0.8546787 0.2689505 0.6796950 0.02235351 0.03937749 0.011646099
## 34 34 0.8545418 0.2691788 0.6794022 0.02227961 0.03947556 0.011556022
## 35 35 0.8549708 0.2684493 0.6801318 0.02185350 0.03882313 0.011507117
## 36 36 0.8555460 0.2675421 0.6804990 0.02196343 0.03861953 0.011306190
## 37 37 0.8552748 0.2679953 0.6804133 0.02189799 0.03852539 0.011774412
## 38 38 0.8558140 0.2671356 0.6808794 0.02170943 0.03821526 0.011325822
## 39 39 0.8560290 0.2668325 0.6809451 0.02205444 0.03812113 0.011325417
## 40 40 0.8562127 0.2664942 0.6810055 0.02198948 0.03805974 0.011233690
## 41 41 0.8565916 0.2658941 0.6811728 0.02179964 0.03775803 0.011114039
## 42 42 0.8564008 0.2661526 0.6810091 0.02103147 0.03735151 0.010588153
## 43 43 0.8565398 0.2659343 0.6810375 0.02136343 0.03770645 0.010988505
## 44 44 0.8564356 0.2661311 0.6807103 0.02102999 0.03762771 0.011057399
## 45 45 0.8566405 0.2658594 0.6808431 0.02157039 0.03795943 0.011174740
## 46 46 0.8569661 0.2653589 0.6811835 0.02140732 0.03755600 0.010967479
## 47 47 0.8572349 0.2649524 0.6813495 0.02193827 0.03803486 0.011240758
## 48 48 0.8575940 0.2643620 0.6817615 0.02199235 0.03823065 0.011328661
## 49 49 0.8577924 0.2640817 0.6819810 0.02236332 0.03870643 0.011599964
## 50 50 0.8580384 0.2637006 0.6817874 0.02226482 0.03891848 0.011695695
## 51 51 0.8588178 0.2624359 0.6822169 0.02232601 0.03855421 0.011660920
## 52 52 0.8592334 0.2617732 0.6823859 0.02250139 0.03864128 0.011688612
## 53 53 0.8594251 0.2614863 0.6825229 0.02252013 0.03915174 0.011607916
## 54 54 0.8596801 0.2611223 0.6828582 0.02282794 0.03982802 0.011766798
## 55 55 0.8597036 0.2610845 0.6828648 0.02307534 0.03961927 0.011777666
## 56 56 0.8599495 0.2606877 0.6830403 0.02327308 0.03982076 0.011985187
## 57 57 0.8599219 0.2607450 0.6831402 0.02304228 0.03950234 0.011768947
## 58 58 0.8602436 0.2602361 0.6833939 0.02302300 0.03944911 0.011919769
## 59 59 0.8604636 0.2599016 0.6835385 0.02335329 0.04018387 0.011943676
## 60 60 0.8607547 0.2594935 0.6839073 0.02337382 0.04015811 0.011730696
## 61 61 0.8606878 0.2595798 0.6839600 0.02299717 0.03998111 0.011527495
## 62 62 0.8611273 0.2589191 0.6842656 0.02273221 0.03965278 0.011458587
## 63 63 0.8613545 0.2585633 0.6845327 0.02272846 0.03960151 0.011478245
## 64 64 0.8616041 0.2582266 0.6848358 0.02301646 0.04006241 0.011763177
## 65 65 0.8616298 0.2582115 0.6850070 0.02292652 0.03979662 0.011411143
## 66 66 0.8615680 0.2583130 0.6850001 0.02323994 0.04000469 0.011397776
## 67 67 0.8616065 0.2582328 0.6849396 0.02300880 0.03995993 0.011438906
## 68 68 0.8619121 0.2577258 0.6853149 0.02297638 0.03948560 0.011569265
## 69 69 0.8620010 0.2575996 0.6855489 0.02303470 0.03948382 0.011587113
## 70 70 0.8621876 0.2573172 0.6857408 0.02296062 0.03985317 0.011704253
## 71 71 0.8624907 0.2569009 0.6859087 0.02310003 0.04000739 0.011948345
## 72 72 0.8622932 0.2572212 0.6859003 0.02340283 0.03984064 0.012097932
## 73 73 0.8624180 0.2570222 0.6859880 0.02317919 0.03992050 0.011906265
## 74 74 0.8621152 0.2575276 0.6858810 0.02289644 0.03942985 0.011624615
## 75 75 0.8624383 0.2570587 0.6861368 0.02298373 0.03902107 0.011629957
## 76 76 0.8625528 0.2568969 0.6862417 0.02296016 0.03919619 0.011642372
## 77 77 0.8625453 0.2569403 0.6861738 0.02276174 0.03910593 0.011609786
## 78 78 0.8621474 0.2575449 0.6859454 0.02282321 0.03892175 0.011481268
## 79 79 0.8622038 0.2574533 0.6860222 0.02265113 0.03882590 0.011527443
## 80 80 0.8622496 0.2573860 0.6861336 0.02254530 0.03858594 0.011463362
## 81 81 0.8623784 0.2571766 0.6862624 0.02260998 0.03865733 0.011591014
## 82 82 0.8625536 0.2569046 0.6864175 0.02260920 0.03870323 0.011557710
## 83 83 0.8626205 0.2568160 0.6864811 0.02260591 0.03888756 0.011534196
## 84 84 0.8627555 0.2566066 0.6865974 0.02247749 0.03856659 0.011471017
## 85 85 0.8627816 0.2566244 0.6865651 0.02286595 0.03909097 0.011747462
## 86 86 0.8628420 0.2565855 0.6865279 0.02280113 0.03935743 0.011528747
## 87 87 0.8627422 0.2567764 0.6863802 0.02284789 0.03966106 0.011848689
## 88 88 0.8629300 0.2565071 0.6866068 0.02287053 0.03980878 0.012095441
## 89 89 0.8629683 0.2564434 0.6868000 0.02282260 0.03979448 0.012083280
## 90 90 0.8629137 0.2565476 0.6866984 0.02253930 0.03957695 0.011942373
## 91 91 0.8629893 0.2564289 0.6866892 0.02249956 0.03973792 0.011908955
## 92 92 0.8631752 0.2561613 0.6867849 0.02242927 0.03983101 0.011909856
## 93 93 0.8633571 0.2558983 0.6869199 0.02245292 0.03976522 0.011878171
## 94 94 0.8638168 0.2551766 0.6872707 0.02251699 0.03942819 0.011648506
## 95 95 0.8638216 0.2551740 0.6871601 0.02257876 0.03944053 0.011893657
## 96 96 0.8637762 0.2552840 0.6871223 0.02250355 0.03971598 0.011707383
## 97 97 0.8639722 0.2549872 0.6872561 0.02255294 0.03967591 0.011714337
## 98 98 0.8639248 0.2550571 0.6872591 0.02269389 0.03982203 0.011733838
## 99 99 0.8637976 0.2552673 0.6872833 0.02292304 0.03966988 0.012089633
## 100 100 0.8639276 0.2550751 0.6873494 0.02283134 0.03958488 0.012119770
## 101 101 0.8639310 0.2550947 0.6873294 0.02308219 0.03973637 0.012483451
## 102 102 0.8641214 0.2547996 0.6875137 0.02294089 0.03948148 0.012453598
## 103 103 0.8640438 0.2549026 0.6874782 0.02260457 0.03958040 0.012331183
## 104 104 0.8639971 0.2549554 0.6874443 0.02239359 0.03932374 0.012277081
## 105 105 0.8638146 0.2552952 0.6873980 0.02242931 0.03945707 0.012272337
## 106 106 0.8641285 0.2548169 0.6876629 0.02249397 0.03957512 0.012301427
## 107 107 0.8640896 0.2549081 0.6874165 0.02256714 0.03951018 0.012320135
## 108 108 0.8641048 0.2548412 0.6874022 0.02242589 0.03935083 0.012446474
## 109 109 0.8641617 0.2547380 0.6873514 0.02252793 0.03964234 0.012504004
## 110 110 0.8642098 0.2546959 0.6873772 0.02278292 0.03985004 0.012625401
## 111 111 0.8640482 0.2549814 0.6872809 0.02291029 0.03993400 0.012747840
## 112 112 0.8640244 0.2550357 0.6869920 0.02301998 0.04001106 0.012780784
## 113 113 0.8639652 0.2551343 0.6869245 0.02292857 0.03992673 0.012710565
## 114 114 0.8639743 0.2550847 0.6869271 0.02293254 0.03984016 0.012523956
## 115 115 0.8642209 0.2547209 0.6870939 0.02290239 0.04002515 0.012429189
## 116 116 0.8643167 0.2545824 0.6870455 0.02274262 0.03976835 0.012325218
## 117 117 0.8645242 0.2542777 0.6871576 0.02277597 0.03963868 0.012464669
## 118 118 0.8646938 0.2540354 0.6872503 0.02268876 0.03972119 0.012425397
## 119 119 0.8649579 0.2536270 0.6874933 0.02271760 0.03986520 0.012513436
## 120 120 0.8649371 0.2536700 0.6874823 0.02287068 0.03993748 0.012553750
## 121 121 0.8648339 0.2538322 0.6874138 0.02298608 0.03991887 0.012754798
## 122 122 0.8647333 0.2540264 0.6875500 0.02310457 0.04014672 0.012897937
## 123 123 0.8646267 0.2542404 0.6874359 0.02316639 0.04022057 0.012785627
## 124 124 0.8645869 0.2543033 0.6875010 0.02322197 0.04010865 0.012838807
## 125 125 0.8646461 0.2541968 0.6876069 0.02317220 0.04026107 0.012871946
## 126 126 0.8645924 0.2542667 0.6875251 0.02307234 0.03999936 0.012739342
## 127 127 0.8646980 0.2541050 0.6876729 0.02306944 0.04010978 0.012849229
## 128 128 0.8646428 0.2542081 0.6875173 0.02294934 0.04003712 0.012713484
## 129 129 0.8647113 0.2541212 0.6875933 0.02284774 0.03977380 0.012700798
## 130 130 0.8647358 0.2540712 0.6877752 0.02281343 0.03962359 0.012609551
## 131 131 0.8648609 0.2538915 0.6878855 0.02272326 0.03941823 0.012574837
## 132 132 0.8648319 0.2539574 0.6877861 0.02273318 0.03936173 0.012691553
## 133 133 0.8649105 0.2538527 0.6878333 0.02294705 0.03947070 0.012981433
## 134 134 0.8650420 0.2536625 0.6879702 0.02292603 0.03970010 0.013139260
## 135 135 0.8650928 0.2536056 0.6879470 0.02305873 0.04002251 0.013223251
## 136 136 0.8648953 0.2539091 0.6878607 0.02287449 0.04011945 0.013138043
## 137 137 0.8650255 0.2537175 0.6879611 0.02276319 0.04013553 0.013046224
## 138 138 0.8649903 0.2537767 0.6879718 0.02277112 0.04011856 0.013034614
## 139 139 0.8649349 0.2538561 0.6879858 0.02274462 0.04025750 0.013035527
## 140 140 0.8649034 0.2539040 0.6879633 0.02269025 0.04018077 0.012953284
## 141 141 0.8649193 0.2538974 0.6878754 0.02272227 0.04020676 0.013016631
## 142 142 0.8648729 0.2539898 0.6879420 0.02274411 0.04018747 0.013118582
## 143 143 0.8649622 0.2538488 0.6879913 0.02259673 0.04014691 0.013031794
## 144 144 0.8649820 0.2538330 0.6879869 0.02262471 0.04000037 0.013051627
## 145 145 0.8648718 0.2540148 0.6878303 0.02270151 0.03980715 0.013046133
## 146 146 0.8649564 0.2538647 0.6879425 0.02261232 0.03970548 0.013038314
## 147 147 0.8649800 0.2538379 0.6879495 0.02260836 0.03978784 0.013203084
## 148 148 0.8649653 0.2538458 0.6879608 0.02271157 0.03964422 0.013328384
## 149 149 0.8650108 0.2537992 0.6880340 0.02272053 0.03977841 0.013341805
## 150 150 0.8651220 0.2536244 0.6880882 0.02262437 0.03955008 0.013285695
## 151 151 0.8651755 0.2535563 0.6880471 0.02269941 0.03958231 0.013277294
## 152 152 0.8650761 0.2537128 0.6879846 0.02271006 0.03939514 0.013359954
## 153 153 0.8650536 0.2537715 0.6880018 0.02271943 0.03957421 0.013380857
## 154 154 0.8649925 0.2538601 0.6880135 0.02259248 0.03939956 0.013376176
## 155 155 0.8649516 0.2539226 0.6880343 0.02255573 0.03940979 0.013407221
## 156 156 0.8648686 0.2540589 0.6879204 0.02264680 0.03951699 0.013477016
## 157 157 0.8650065 0.2538640 0.6880397 0.02258333 0.03963190 0.013452779
## 158 158 0.8649871 0.2539021 0.6879411 0.02263934 0.03970666 0.013386977
## 159 159 0.8651086 0.2537065 0.6880230 0.02268426 0.03953457 0.013395536
## 160 160 0.8651160 0.2536991 0.6880874 0.02285787 0.03974293 0.013594024
## 161 161 0.8650613 0.2537753 0.6879290 0.02287940 0.03961053 0.013601198
## 162 162 0.8650488 0.2537993 0.6879546 0.02278189 0.03962506 0.013450968
## 163 163 0.8650231 0.2538287 0.6878645 0.02276132 0.03953795 0.013403980
## 164 164 0.8648944 0.2540423 0.6878135 0.02278099 0.03963036 0.013345888
## 165 165 0.8649215 0.2540099 0.6879079 0.02282972 0.03963680 0.013360375
## 166 166 0.8648713 0.2540927 0.6878505 0.02286969 0.03969039 0.013394311
## 167 167 0.8649512 0.2539690 0.6878033 0.02282400 0.03953047 0.013353501
## 168 168 0.8649285 0.2540071 0.6877612 0.02276059 0.03939986 0.013316590
## 169 169 0.8648972 0.2540578 0.6876413 0.02258711 0.03943131 0.013231809
## 170 170 0.8648942 0.2540518 0.6876403 0.02262459 0.03953507 0.013224188
## 171 171 0.8649062 0.2540396 0.6875762 0.02258325 0.03959937 0.013193407
## 172 172 0.8650566 0.2538108 0.6877447 0.02247007 0.03961119 0.013142999
## 173 173 0.8650676 0.2537694 0.6877975 0.02230431 0.03939524 0.012959351
## 174 174 0.8650000 0.2538676 0.6877197 0.02230395 0.03921615 0.012826886
## 175 175 0.8649376 0.2539730 0.6876140 0.02239028 0.03945357 0.012853691
## 176 176 0.8648355 0.2541163 0.6875443 0.02240521 0.03936185 0.012897293
## 177 177 0.8648765 0.2540552 0.6875267 0.02237418 0.03943439 0.012818691
## 178 178 0.8648786 0.2540415 0.6874495 0.02236134 0.03935604 0.012809291
## 179 179 0.8648010 0.2541530 0.6874119 0.02229807 0.03925081 0.012750033
## 180 180 0.8647967 0.2541562 0.6874390 0.02224118 0.03921167 0.012710925
## 181 181 0.8647086 0.2542935 0.6873807 0.02226099 0.03931208 0.012726684
## 182 182 0.8646611 0.2543636 0.6873609 0.02224473 0.03930673 0.012678117
## 183 183 0.8647279 0.2542700 0.6874623 0.02223169 0.03931377 0.012678377
## 184 184 0.8646002 0.2544690 0.6873438 0.02226686 0.03931839 0.012720786
## 185 185 0.8644855 0.2546544 0.6872713 0.02231349 0.03940733 0.012745724
## 186 186 0.8645108 0.2546150 0.6873103 0.02241869 0.03952603 0.012811135
## 187 187 0.8645275 0.2545961 0.6873216 0.02234788 0.03942737 0.012825024
## 188 188 0.8645064 0.2546186 0.6873170 0.02236477 0.03934974 0.012832677
## 189 189 0.8645203 0.2546023 0.6872930 0.02242791 0.03935677 0.012873482
## 190 190 0.8645173 0.2546063 0.6873051 0.02239633 0.03925129 0.012846322
## 191 191 0.8644846 0.2546522 0.6872394 0.02244408 0.03920962 0.012874803
## 192 192 0.8644630 0.2546868 0.6872773 0.02241446 0.03921346 0.012851170
## 193 193 0.8644793 0.2546620 0.6872566 0.02249505 0.03924786 0.012886347
## 194 194 0.8643716 0.2548227 0.6871672 0.02260590 0.03920802 0.012994150
## 195 195 0.8643247 0.2549015 0.6871332 0.02267170 0.03927658 0.013057683
## 196 196 0.8642979 0.2549506 0.6871421 0.02266730 0.03931860 0.013036793
## 197 197 0.8642901 0.2549610 0.6871346 0.02264930 0.03936633 0.012982213
## 198 198 0.8643392 0.2548873 0.6871532 0.02260894 0.03932886 0.012982648
## 199 199 0.8643977 0.2548031 0.6872204 0.02262645 0.03934463 0.012974638
## 200 200 0.8644362 0.2547429 0.6872425 0.02260962 0.03929912 0.012936024
## 201 201 0.8644549 0.2547207 0.6872944 0.02262497 0.03934129 0.012912525
## 202 202 0.8645196 0.2546250 0.6873477 0.02267341 0.03933872 0.012939766
## 203 203 0.8645116 0.2546414 0.6873200 0.02265484 0.03933973 0.012960228
## 204 204 0.8644469 0.2547392 0.6872751 0.02269068 0.03936621 0.013013217
## 205 205 0.8643884 0.2548245 0.6872247 0.02268476 0.03938514 0.013061549
## 206 206 0.8644334 0.2547485 0.6873006 0.02269867 0.03936913 0.013078552
## 207 207 0.8644446 0.2547308 0.6872961 0.02269099 0.03937424 0.013078325
## 208 208 0.8643692 0.2548497 0.6872574 0.02273320 0.03939420 0.013067579
## 209 209 0.8643791 0.2548292 0.6872379 0.02267944 0.03937843 0.013053887
## 210 210 0.8644623 0.2546983 0.6873041 0.02266541 0.03939790 0.013031885
## 211 211 0.8644711 0.2546796 0.6872596 0.02260680 0.03933482 0.013000818
## 212 212 0.8644700 0.2546716 0.6873007 0.02256364 0.03920865 0.012984019
## 213 213 0.8644564 0.2546894 0.6873028 0.02257493 0.03919300 0.012973622
## 214 214 0.8644395 0.2547147 0.6872950 0.02254866 0.03915731 0.012994967
## 215 215 0.8644533 0.2547003 0.6873115 0.02254708 0.03917199 0.013027569
## 216 216 0.8644245 0.2547440 0.6872982 0.02250888 0.03910549 0.013009636
## 217 217 0.8643835 0.2548102 0.6872651 0.02254643 0.03914950 0.013050115
## 218 218 0.8643650 0.2548346 0.6872318 0.02253205 0.03914649 0.013068420
## 219 219 0.8643674 0.2548320 0.6872374 0.02249098 0.03911001 0.013042970
## 220 220 0.8643969 0.2547848 0.6872646 0.02247126 0.03908679 0.013036641
## 221 221 0.8644215 0.2547497 0.6872928 0.02250089 0.03909540 0.013039164
## 222 222 0.8644478 0.2547095 0.6873248 0.02252380 0.03909233 0.013045194
## 223 223 0.8644123 0.2547637 0.6872991 0.02251835 0.03908691 0.013055552
## 224 224 0.8644113 0.2547644 0.6873002 0.02251647 0.03907481 0.013060854
## 225 225 0.8644143 0.2547603 0.6872968 0.02249715 0.03902484 0.013040494
## 226 226 0.8644059 0.2547721 0.6872893 0.02252592 0.03906091 0.013054429
## 227 227 0.8644351 0.2547275 0.6873143 0.02253630 0.03907459 0.013069680
## 228 228 0.8644320 0.2547320 0.6873170 0.02253255 0.03907449 0.013064895
## 229 229 0.8644077 0.2547698 0.6872986 0.02253796 0.03908791 0.013070851
## 230 230 0.8644094 0.2547651 0.6873059 0.02254183 0.03909273 0.013078633
## 231 231 0.8644020 0.2547747 0.6872930 0.02253830 0.03909598 0.013083967
## 232 232 0.8644039 0.2547722 0.6873020 0.02253601 0.03909419 0.013077635
## 233 233 0.8644071 0.2547669 0.6873097 0.02253836 0.03910302 0.013080915
## 234 234 0.8643971 0.2547811 0.6873031 0.02253398 0.03908905 0.013073840
## 235 235 0.8643967 0.2547820 0.6873027 0.02253445 0.03909576 0.013074208
## 236 236 0.8644035 0.2547713 0.6873088 0.02253801 0.03910281 0.013074828
## 237 237 0.8644062 0.2547673 0.6873107 0.02253661 0.03909711 0.013068239
## 238 238 0.8644052 0.2547685 0.6873100 0.02253263 0.03909217 0.013064110
## 239 239 0.8644026 0.2547719 0.6873081 0.02253208 0.03909270 0.013066488
## 240 240 0.8644000 0.2547759 0.6873056 0.02253218 0.03909530 0.013066612
## nvmax
## 11 11
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Coefficients of final model:
## (Intercept) x4 x7 x9 x10
## -3.107249329 -0.001286918 0.338846892 0.098579372 0.037105764
## x16 x17 x21 stat14 stat98
## 0.026656795 0.033446343 0.004230119 -0.023583076 0.103757551
## stat110 sqrt.x18
## -0.094831072 0.800160707Test
if (algo.backward.caret == TRUE){
test.model(model.backward, data.test
,method = 'leapBackward',subopt = NULL
,formula = formula, feature.names = feature.names, label.names = label.names
,id = id
,draw.limits = TRUE, transformation = t)
}## [1] "Summary of predicted values: "
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -1.73261 -0.36478 -0.01048 -0.01629 0.36378 1.30380
## [1] "leapBackward Test MSE: 0.747502177162688"
Backward Elimination with CV (w/ filtered train)
Train
if (algo.backward.caret == TRUE){
set.seed(1)
returned = train.caret.glmselect(formula = formula
,data = data.train2
,method = "leapBackward"
,feature.names = feature.names)
model.backward = returned$model
id = returned$id
}## Aggregating results
## Selecting tuning parameters
## Fitting nvmax = 16 on full training set
## nvmax RMSE Rsquared MAE RMSESD RsquaredSD MAESD
## 1 1 0.8324519 0.1749919 0.6790566 0.01597113 0.03921811 0.01317663
## 2 2 0.7975540 0.2419521 0.6557232 0.01520566 0.03509192 0.01335539
## 3 3 0.7750513 0.2839167 0.6353240 0.01931842 0.02940618 0.01851574
## 4 4 0.7546417 0.3209165 0.6150965 0.01930866 0.02741817 0.01668461
## 5 5 0.7432158 0.3417524 0.6063017 0.02162484 0.02238676 0.01910499
## 6 6 0.7401442 0.3470278 0.6037990 0.02181541 0.02075352 0.02001697
## 7 7 0.7388292 0.3491892 0.6041669 0.01940552 0.01914209 0.01848950
## 8 8 0.7383222 0.3500468 0.6038449 0.01932533 0.01885603 0.01744368
## 9 9 0.7359585 0.3540996 0.6023114 0.01880218 0.01718949 0.01713525
## 10 10 0.7340769 0.3573473 0.6008371 0.01896156 0.01735750 0.01725500
## 11 11 0.7323836 0.3603312 0.5991209 0.01940450 0.01799580 0.01818901
## 12 12 0.7333335 0.3587364 0.6004735 0.01960659 0.01699529 0.01835213
## 13 13 0.7332249 0.3589732 0.6005063 0.01986340 0.01808885 0.01849686
## 14 14 0.7330207 0.3593675 0.6002626 0.02005035 0.01830787 0.01879488
## 15 15 0.7325484 0.3601726 0.5999397 0.01917544 0.01801880 0.01790246
## 16 16 0.7317308 0.3616345 0.5988380 0.02011027 0.01783930 0.01840313
## 17 17 0.7322342 0.3607858 0.5991575 0.02040800 0.01736870 0.01864032
## 18 18 0.7324076 0.3605535 0.5993948 0.02081518 0.01690330 0.01909928
## 19 19 0.7327134 0.3600782 0.5996675 0.02115503 0.01584226 0.01967158
## 20 20 0.7327574 0.3599987 0.6001065 0.02116280 0.01509935 0.01986224
## 21 21 0.7330630 0.3594838 0.6007861 0.02163138 0.01613652 0.01997785
## 22 22 0.7330684 0.3595181 0.6006919 0.02164248 0.01686002 0.02016024
## 23 23 0.7330937 0.3594870 0.6011990 0.02099292 0.01643847 0.02001302
## 24 24 0.7335244 0.3587289 0.6011943 0.02028905 0.01625118 0.02006790
## 25 25 0.7340471 0.3578715 0.6016609 0.02088606 0.01663591 0.02073558
## 26 26 0.7341923 0.3576891 0.6015894 0.02186515 0.01729934 0.02111070
## 27 27 0.7345717 0.3570480 0.6018002 0.02169910 0.01755480 0.02071410
## 28 28 0.7344784 0.3572508 0.6020575 0.02184505 0.01701667 0.02060297
## 29 29 0.7349207 0.3565489 0.6023176 0.02197727 0.01729729 0.02073836
## 30 30 0.7350268 0.3563852 0.6023190 0.02167079 0.01817402 0.02068993
## 31 31 0.7351375 0.3562229 0.6021529 0.02164080 0.01894575 0.02098779
## 32 32 0.7357272 0.3552410 0.6024485 0.02206709 0.01994995 0.02105072
## 33 33 0.7365305 0.3539364 0.6031123 0.02210498 0.02036133 0.02132365
## 34 34 0.7367734 0.3535593 0.6035696 0.02223409 0.01935169 0.02136655
## 35 35 0.7371631 0.3529528 0.6036269 0.02248105 0.01987008 0.02128128
## 36 36 0.7377646 0.3519296 0.6039344 0.02241741 0.02080523 0.02122380
## 37 37 0.7377886 0.3518938 0.6041700 0.02241736 0.02054585 0.02116669
## 38 38 0.7378379 0.3518580 0.6043616 0.02263703 0.02133994 0.02137927
## 39 39 0.7379033 0.3517770 0.6043544 0.02264682 0.02146249 0.02139431
## 40 40 0.7377248 0.3520466 0.6041923 0.02243992 0.02176924 0.02120984
## 41 41 0.7377956 0.3519233 0.6043662 0.02223299 0.02192225 0.02091029
## 42 42 0.7378202 0.3519570 0.6043647 0.02220859 0.02152514 0.02087119
## 43 43 0.7380122 0.3516398 0.6042314 0.02196277 0.02103358 0.02086983
## 44 44 0.7383819 0.3510828 0.6044223 0.02219104 0.02086301 0.02079454
## 45 45 0.7381989 0.3514394 0.6041294 0.02173237 0.02006428 0.02037224
## 46 46 0.7382611 0.3514142 0.6041047 0.02177508 0.02012348 0.02056553
## 47 47 0.7384513 0.3510992 0.6042864 0.02134410 0.02026912 0.01997986
## 48 48 0.7390096 0.3502030 0.6045269 0.02145013 0.02033286 0.02015334
## 49 49 0.7391026 0.3501302 0.6044283 0.02197904 0.02049242 0.02045480
## 50 50 0.7395125 0.3494579 0.6049659 0.02157960 0.01978338 0.02000360
## 51 51 0.7395869 0.3493532 0.6050745 0.02174456 0.01997547 0.02022909
## 52 52 0.7399930 0.3487315 0.6055198 0.02220767 0.02081935 0.02067016
## 53 53 0.7403258 0.3481835 0.6057741 0.02254888 0.02131872 0.02116833
## 54 54 0.7403646 0.3481632 0.6057626 0.02279238 0.02198723 0.02149358
## 55 55 0.7405578 0.3479075 0.6060675 0.02255719 0.02150829 0.02129599
## 56 56 0.7402675 0.3483648 0.6057983 0.02247447 0.02157298 0.02125275
## 57 57 0.7400713 0.3486941 0.6057415 0.02248535 0.02186180 0.02112776
## 58 58 0.7398457 0.3490980 0.6055815 0.02277168 0.02188949 0.02137303
## 59 59 0.7396393 0.3494755 0.6054555 0.02256942 0.02168052 0.02130426
## 60 60 0.7399989 0.3488604 0.6056594 0.02258464 0.02197369 0.02111364
## 61 61 0.7394520 0.3498082 0.6052275 0.02273897 0.02188312 0.02111116
## 62 62 0.7391492 0.3503066 0.6049003 0.02259167 0.02200052 0.02088273
## 63 63 0.7388801 0.3507553 0.6046626 0.02263070 0.02239225 0.02073829
## 64 64 0.7385731 0.3512850 0.6044165 0.02250421 0.02241503 0.02068141
## 65 65 0.7389032 0.3507260 0.6046745 0.02196902 0.02188999 0.02029436
## 66 66 0.7386691 0.3511145 0.6045303 0.02137111 0.02123239 0.02000717
## 67 67 0.7384457 0.3514930 0.6043414 0.02120997 0.02099713 0.01991904
## 68 68 0.7380927 0.3521267 0.6042541 0.02088386 0.02089647 0.01985005
## 69 69 0.7379604 0.3523613 0.6040993 0.02081242 0.02064170 0.01975116
## 70 70 0.7383222 0.3517431 0.6041050 0.02038979 0.02101562 0.01958006
## 71 71 0.7381147 0.3520817 0.6039872 0.02017274 0.02127331 0.01957448
## 72 72 0.7379961 0.3523173 0.6037773 0.02027256 0.02106071 0.01953018
## 73 73 0.7382875 0.3518242 0.6038303 0.02054508 0.02181250 0.01973303
## 74 74 0.7376546 0.3528747 0.6032381 0.02041644 0.02236972 0.01971345
## 75 75 0.7376373 0.3529019 0.6033699 0.02036470 0.02143375 0.01991639
## 76 76 0.7380230 0.3523338 0.6035080 0.02056881 0.02122685 0.02009105
## 77 77 0.7376924 0.3529237 0.6032176 0.02111542 0.02098117 0.02065105
## 78 78 0.7372616 0.3536971 0.6029380 0.02091920 0.02077267 0.02045711
## 79 79 0.7370484 0.3540460 0.6027298 0.02082011 0.02023735 0.02050815
## 80 80 0.7370506 0.3540474 0.6030472 0.02067651 0.02021047 0.02043272
## 81 81 0.7371030 0.3539551 0.6032664 0.02053770 0.01948618 0.02031803
## 82 82 0.7365909 0.3548262 0.6029136 0.02068908 0.01917160 0.02045268
## 83 83 0.7365162 0.3549564 0.6028117 0.02075793 0.01890449 0.02047765
## 84 84 0.7361909 0.3555405 0.6025092 0.02105289 0.01897186 0.02089617
## 85 85 0.7361063 0.3556900 0.6024716 0.02105655 0.01939723 0.02105938
## 86 86 0.7360610 0.3557874 0.6023801 0.02085794 0.01957342 0.02079779
## 87 87 0.7357875 0.3562441 0.6021318 0.02058090 0.01963295 0.02060730
## 88 88 0.7358907 0.3560597 0.6021471 0.02057698 0.01956469 0.02052825
## 89 89 0.7358817 0.3561142 0.6019263 0.02027123 0.01944037 0.02006779
## 90 90 0.7357122 0.3563669 0.6016857 0.01990913 0.01958328 0.01973794
## 91 91 0.7356018 0.3566012 0.6017060 0.02013723 0.02001663 0.01985737
## 92 92 0.7358950 0.3561089 0.6016946 0.01997946 0.02016710 0.01959512
## 93 93 0.7358142 0.3562424 0.6016055 0.02022926 0.02069673 0.01979007
## 94 94 0.7355831 0.3566789 0.6014604 0.02040330 0.02084753 0.02003126
## 95 95 0.7353561 0.3570438 0.6014616 0.02048401 0.02127502 0.02018430
## 96 96 0.7351586 0.3573851 0.6013999 0.02071498 0.02108858 0.02055617
## 97 97 0.7353867 0.3570005 0.6016002 0.02085758 0.02169963 0.02071234
## 98 98 0.7353555 0.3570594 0.6015042 0.02080017 0.02148410 0.02057996
## 99 99 0.7353622 0.3570427 0.6014576 0.02077198 0.02156498 0.02042626
## 100 100 0.7353214 0.3571289 0.6014759 0.02088673 0.02166987 0.02056860
## 101 101 0.7352321 0.3572884 0.6014275 0.02080791 0.02175750 0.02035115
## 102 102 0.7353037 0.3571574 0.6014730 0.02080936 0.02201608 0.02049454
## 103 103 0.7354449 0.3569527 0.6015833 0.02071500 0.02211927 0.02045860
## 104 104 0.7354403 0.3569919 0.6015772 0.02064450 0.02197401 0.02029978
## 105 105 0.7355631 0.3568147 0.6018835 0.02054115 0.02179020 0.02018713
## 106 106 0.7352714 0.3573585 0.6017191 0.02059763 0.02177344 0.02027878
## 107 107 0.7352532 0.3573935 0.6017625 0.02070919 0.02207279 0.02061441
## 108 108 0.7351297 0.3576209 0.6017504 0.02042177 0.02186825 0.02047120
## 109 109 0.7352548 0.3574351 0.6019641 0.02010981 0.02196528 0.02020739
## 110 110 0.7352478 0.3574638 0.6018866 0.02023271 0.02256207 0.02014932
## 111 111 0.7350903 0.3577462 0.6018518 0.02025268 0.02221669 0.02013073
## 112 112 0.7349527 0.3579684 0.6017193 0.02030928 0.02250602 0.02005199
## 113 113 0.7350037 0.3578944 0.6018728 0.02021089 0.02265954 0.02002203
## 114 114 0.7347123 0.3583867 0.6015191 0.02009273 0.02233066 0.01995680
## 115 115 0.7346044 0.3585573 0.6014961 0.02014510 0.02288685 0.01988696
## 116 116 0.7346919 0.3584145 0.6016642 0.01989106 0.02285050 0.01963548
## 117 117 0.7347585 0.3582850 0.6018171 0.01976057 0.02298913 0.01940685
## 118 118 0.7348088 0.3582398 0.6018697 0.01985867 0.02314235 0.01943144
## 119 119 0.7350112 0.3579323 0.6021152 0.01980534 0.02330670 0.01932507
## 120 120 0.7350437 0.3579291 0.6021227 0.01999448 0.02307898 0.01951638
## 121 121 0.7349793 0.3580418 0.6022564 0.02041049 0.02307206 0.01970916
## 122 122 0.7349855 0.3580319 0.6021479 0.02053932 0.02297281 0.01982730
## 123 123 0.7347974 0.3583421 0.6020922 0.02047981 0.02328953 0.01981486
## 124 124 0.7349574 0.3580903 0.6021773 0.02047346 0.02349197 0.01969752
## 125 125 0.7349567 0.3581067 0.6021455 0.02066831 0.02355930 0.01983274
## 126 126 0.7348236 0.3583227 0.6019909 0.02062007 0.02356245 0.01973303
## 127 127 0.7346960 0.3585651 0.6019076 0.02046788 0.02302864 0.01972127
## 128 128 0.7346109 0.3587193 0.6019976 0.02055486 0.02302554 0.01979572
## 129 129 0.7347967 0.3584095 0.6020601 0.02037043 0.02322615 0.01964232
## 130 130 0.7346325 0.3586828 0.6017961 0.02035625 0.02307433 0.01960182
## 131 131 0.7347794 0.3584707 0.6019710 0.02060354 0.02320372 0.01981102
## 132 132 0.7347681 0.3585086 0.6021559 0.02074723 0.02357793 0.01986750
## 133 133 0.7346875 0.3586601 0.6020694 0.02074149 0.02365907 0.01990056
## 134 134 0.7346256 0.3587775 0.6020205 0.02079427 0.02361278 0.01986977
## 135 135 0.7347838 0.3585316 0.6021457 0.02094536 0.02318253 0.02002094
## 136 136 0.7349166 0.3583194 0.6021944 0.02085331 0.02285111 0.01980319
## 137 137 0.7349252 0.3583012 0.6021883 0.02065148 0.02256738 0.01960959
## 138 138 0.7350796 0.3580551 0.6023829 0.02059870 0.02233765 0.01962065
## 139 139 0.7350692 0.3580915 0.6023115 0.02065969 0.02254301 0.01964570
## 140 140 0.7351157 0.3580279 0.6023768 0.02066424 0.02275451 0.01967534
## 141 141 0.7351034 0.3580395 0.6022575 0.02065141 0.02266576 0.01966388
## 142 142 0.7351953 0.3578844 0.6024235 0.02065282 0.02275077 0.01970607
## 143 143 0.7352703 0.3577720 0.6024299 0.02061649 0.02265158 0.01959664
## 144 144 0.7353458 0.3576544 0.6025113 0.02064246 0.02269462 0.01961263
## 145 145 0.7353549 0.3576358 0.6025362 0.02057332 0.02282067 0.01954126
## 146 146 0.7353703 0.3576146 0.6024765 0.02055004 0.02317644 0.01943650
## 147 147 0.7354947 0.3574378 0.6025417 0.02051038 0.02301323 0.01934286
## 148 148 0.7353145 0.3577338 0.6024540 0.02041604 0.02301861 0.01932420
## 149 149 0.7354543 0.3575025 0.6025727 0.02051662 0.02285256 0.01940678
## 150 150 0.7354733 0.3574801 0.6024847 0.02057706 0.02272028 0.01937727
## 151 151 0.7355794 0.3573198 0.6025681 0.02059201 0.02264957 0.01941121
## 152 152 0.7355610 0.3573472 0.6025577 0.02046167 0.02252252 0.01943847
## 153 153 0.7357141 0.3571020 0.6026985 0.02037179 0.02241762 0.01946856
## 154 154 0.7355477 0.3573625 0.6027172 0.02026872 0.02229391 0.01933610
## 155 155 0.7356026 0.3572854 0.6027595 0.02028579 0.02211639 0.01930683
## 156 156 0.7355292 0.3574103 0.6026344 0.02034679 0.02240825 0.01936357
## 157 157 0.7355263 0.3573984 0.6026370 0.02025764 0.02233428 0.01920978
## 158 158 0.7356015 0.3572880 0.6026682 0.02012881 0.02224097 0.01914269
## 159 159 0.7356216 0.3572436 0.6026866 0.02027727 0.02242081 0.01925649
## 160 160 0.7357158 0.3570858 0.6028300 0.02039062 0.02249994 0.01934826
## 161 161 0.7357233 0.3570706 0.6027258 0.02033386 0.02258041 0.01925044
## 162 162 0.7356974 0.3571213 0.6027497 0.02035718 0.02260496 0.01924915
## 163 163 0.7357313 0.3570594 0.6028585 0.02038213 0.02257666 0.01921146
## 164 164 0.7359133 0.3567916 0.6030055 0.02043802 0.02265151 0.01929276
## 165 165 0.7359482 0.3567440 0.6030043 0.02034236 0.02271383 0.01920048
## 166 166 0.7359629 0.3567407 0.6030849 0.02033163 0.02248939 0.01920147
## 167 167 0.7359641 0.3567354 0.6031158 0.02032957 0.02275229 0.01925805
## 168 168 0.7360263 0.3566181 0.6031800 0.02030008 0.02282636 0.01923657
## 169 169 0.7360930 0.3565142 0.6032482 0.02027716 0.02279856 0.01921089
## 170 170 0.7361298 0.3564695 0.6032552 0.02052436 0.02283769 0.01941221
## 171 171 0.7362161 0.3563377 0.6032819 0.02052945 0.02276698 0.01941506
## 172 172 0.7361931 0.3563858 0.6032622 0.02063859 0.02293567 0.01957784
## 173 173 0.7361328 0.3564913 0.6032152 0.02060355 0.02321551 0.01950922
## 174 174 0.7361295 0.3565029 0.6031733 0.02065227 0.02325066 0.01957246
## 175 175 0.7360523 0.3566374 0.6031424 0.02079718 0.02333959 0.01969229
## 176 176 0.7360964 0.3565624 0.6031641 0.02071284 0.02316210 0.01961474
## 177 177 0.7360493 0.3566337 0.6031489 0.02066567 0.02299483 0.01960780
## 178 178 0.7360006 0.3567078 0.6031316 0.02067507 0.02290421 0.01968881
## 179 179 0.7360383 0.3566590 0.6031581 0.02070268 0.02304395 0.01967282
## 180 180 0.7361568 0.3564825 0.6032958 0.02068867 0.02300823 0.01968802
## 181 181 0.7361117 0.3565526 0.6033453 0.02062854 0.02289063 0.01966536
## 182 182 0.7361527 0.3564886 0.6033631 0.02064370 0.02271383 0.01961691
## 183 183 0.7361459 0.3564864 0.6034077 0.02059485 0.02269580 0.01956084
## 184 184 0.7361788 0.3564221 0.6034627 0.02063191 0.02272067 0.01959631
## 185 185 0.7362023 0.3563816 0.6034604 0.02059723 0.02285383 0.01957266
## 186 186 0.7362266 0.3563522 0.6034475 0.02072022 0.02296713 0.01969417
## 187 187 0.7361941 0.3563952 0.6034159 0.02076652 0.02310928 0.01976029
## 188 188 0.7362280 0.3563451 0.6034155 0.02083061 0.02309124 0.01980826
## 189 189 0.7362165 0.3563752 0.6033653 0.02089422 0.02312278 0.01988974
## 190 190 0.7361858 0.3564325 0.6032370 0.02099661 0.02315069 0.01995204
## 191 191 0.7361584 0.3564771 0.6031886 0.02107362 0.02322946 0.01996476
## 192 192 0.7362682 0.3562968 0.6032696 0.02096340 0.02320337 0.01981862
## 193 193 0.7362474 0.3563368 0.6032556 0.02096754 0.02309411 0.01981802
## 194 194 0.7361735 0.3564622 0.6032057 0.02092079 0.02307771 0.01974293
## 195 195 0.7362725 0.3563173 0.6032449 0.02095626 0.02303538 0.01980180
## 196 196 0.7362922 0.3562844 0.6032738 0.02087124 0.02294916 0.01972360
## 197 197 0.7363072 0.3562569 0.6032770 0.02079042 0.02291836 0.01967925
## 198 198 0.7362243 0.3563950 0.6032007 0.02078768 0.02283957 0.01968536
## 199 199 0.7362500 0.3563430 0.6032768 0.02074984 0.02288965 0.01969986
## 200 200 0.7362558 0.3563395 0.6032655 0.02077343 0.02281292 0.01965231
## 201 201 0.7362432 0.3563627 0.6032717 0.02075967 0.02292272 0.01965496
## 202 202 0.7361850 0.3564554 0.6032540 0.02079669 0.02298993 0.01966093
## 203 203 0.7361704 0.3564862 0.6032601 0.02077746 0.02300348 0.01962116
## 204 204 0.7361543 0.3565107 0.6032370 0.02080644 0.02310711 0.01963081
## 205 205 0.7362520 0.3563442 0.6033432 0.02076231 0.02302343 0.01958452
## 206 206 0.7362104 0.3564047 0.6033353 0.02071554 0.02313280 0.01955651
## 207 207 0.7362558 0.3563301 0.6033884 0.02075609 0.02320866 0.01959644
## 208 208 0.7362869 0.3562780 0.6034508 0.02074772 0.02322073 0.01960661
## 209 209 0.7363463 0.3561876 0.6034936 0.02070905 0.02313148 0.01954959
## 210 210 0.7363528 0.3561767 0.6034855 0.02069973 0.02309986 0.01956347
## 211 211 0.7363731 0.3561480 0.6034945 0.02069744 0.02313577 0.01957390
## 212 212 0.7364060 0.3560966 0.6035242 0.02071042 0.02316844 0.01959291
## 213 213 0.7364278 0.3560595 0.6035628 0.02074890 0.02319792 0.01961407
## 214 214 0.7364332 0.3560466 0.6035766 0.02076227 0.02323864 0.01962494
## 215 215 0.7364374 0.3560361 0.6035747 0.02077239 0.02326901 0.01964388
## 216 216 0.7364224 0.3560600 0.6035538 0.02078105 0.02331385 0.01965073
## 217 217 0.7364617 0.3559932 0.6035627 0.02076519 0.02322354 0.01964862
## 218 218 0.7364361 0.3560375 0.6035308 0.02075059 0.02327440 0.01963288
## 219 219 0.7364998 0.3559364 0.6035751 0.02073213 0.02328795 0.01963967
## 220 220 0.7364683 0.3559884 0.6035379 0.02073851 0.02327453 0.01966850
## 221 221 0.7364776 0.3559707 0.6035528 0.02073487 0.02329445 0.01966415
## 222 222 0.7364688 0.3559858 0.6035433 0.02073548 0.02331933 0.01968809
## 223 223 0.7364764 0.3559730 0.6035457 0.02072091 0.02331656 0.01966570
## 224 224 0.7364598 0.3560022 0.6035254 0.02074556 0.02333190 0.01970377
## 225 225 0.7364701 0.3559830 0.6035310 0.02076814 0.02332419 0.01972144
## 226 226 0.7364904 0.3559525 0.6035233 0.02080854 0.02334562 0.01974655
## 227 227 0.7364982 0.3559344 0.6035327 0.02081637 0.02329900 0.01976604
## 228 228 0.7365073 0.3559170 0.6035446 0.02078040 0.02326653 0.01973810
## 229 229 0.7365224 0.3558955 0.6035628 0.02079335 0.02323583 0.01974396
## 230 230 0.7365405 0.3558696 0.6035757 0.02079506 0.02324264 0.01976618
## 231 231 0.7365367 0.3558742 0.6035684 0.02078369 0.02321248 0.01976573
## 232 232 0.7365415 0.3558677 0.6035674 0.02079166 0.02321487 0.01976002
## 233 233 0.7365500 0.3558528 0.6035691 0.02078198 0.02320991 0.01974132
## 234 234 0.7365563 0.3558445 0.6035734 0.02079531 0.02323921 0.01974396
## 235 235 0.7365678 0.3558236 0.6035820 0.02079426 0.02323339 0.01974909
## 236 236 0.7365770 0.3558086 0.6035879 0.02078777 0.02325910 0.01975093
## 237 237 0.7365706 0.3558182 0.6035817 0.02078349 0.02325596 0.01974991
## 238 238 0.7365684 0.3558213 0.6035815 0.02078640 0.02326370 0.01975398
## 239 239 0.7365711 0.3558170 0.6035828 0.02078492 0.02325991 0.01975210
## 240 240 0.7365714 0.3558164 0.6035841 0.02078390 0.02325952 0.01975040
## nvmax
## 16 16
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Coefficients of final model:
## (Intercept) x4 x7 x8 x9
## -3.492157240 -0.001454545 0.355484220 0.012272806 0.101739706
## x10 x13 x16 x17 x21
## 0.043243708 0.006184991 0.024719698 0.035827337 0.004385325
## stat4 stat14 stat41 stat98 stat110
## -0.020060046 -0.029429292 -0.018983255 0.108150933 -0.100901219
## stat144 sqrt.x18
## 0.017815602 0.834483878Test
if (algo.backward.caret == TRUE){
test.model(model.backward, data.test
,method = 'leapBackward',subopt = NULL
,formula = formula, feature.names = feature.names, label.names = label.names
,id = id
,draw.limits = TRUE, transformation = t)
}## [1] "Summary of predicted values: "
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -1.87585 -0.43536 -0.06651 -0.07052 0.31776 1.30819
## [1] "leapBackward Test MSE: 0.7529414468572"
Stepwise Selection (w/ full train)
Train
if (algo.stepwise == TRUE){
t1 = Sys.time()
model.stepwise = step(model.null, scope=list(upper=model.full), data = data.train, direction="both", trace = 0)
print(summary(model.stepwise))
t2 = Sys.time()
print (paste("Time taken for Stepwise Selection: ",t2-t1, sep = ""))
plot.diagnostics(model.stepwise, data.train)
}Test
if (algo.stepwise == TRUE){
test.model(model.stepwise, data.test, "Stepwise Selection")
}Stepwise Selection (w/ filtered train)
Train
if (algo.stepwise == TRUE){
t1 = Sys.time()
model.stepwise2 = step(model.null2, scope=list(upper=model.full2), data = data.train2, direction="both", trace = 0)
print(summary(model.stepwise2))
t2 = Sys.time()
print (paste("Time taken for Stepwise Selection: ",t2-t1, sep = ""))
plot.diagnostics(model.stepwise2, data.train2)
}Test
if (algo.stepwise == TRUE){
test.model(model.stepwise2, data.test, "Stepwise Selection (2)")
}Stepwise Selection with CV (w/ full train)
Train
if (algo.stepwise.caret == TRUE){
set.seed(1)
returned = train.caret.glmselect(formula = formula
,data = data.train
,method = "leapSeq"
,feature.names = feature.names)
model.stepwise = returned$model
id = returned$id
}## Aggregating results
## Selecting tuning parameters
## Fitting nvmax = 12 on full training set
## nvmax RMSE Rsquared MAE RMSESD RsquaredSD MAESD
## 1 1 0.9290962 0.1383305 0.7452032 0.02166207 0.04198587 0.014135754
## 2 2 0.9013733 0.1870866 0.7246099 0.01861952 0.03823400 0.010184999
## 3 3 0.8828368 0.2193324 0.7063992 0.01966356 0.03628172 0.013394655
## 4 4 0.8674643 0.2460619 0.6887616 0.01704415 0.03510583 0.008406016
## 5 5 0.8579880 0.2627745 0.6814284 0.02179327 0.04052670 0.012261767
## 6 6 0.8569684 0.2645498 0.6802092 0.02203645 0.04140563 0.012736876
## 7 7 0.8563457 0.2657719 0.6808046 0.02394403 0.04272918 0.013719134
## 8 8 0.8534905 0.2706668 0.6783829 0.02287015 0.04290888 0.012749598
## 9 9 0.8529722 0.2715124 0.6777699 0.02320165 0.04311993 0.013391833
## 10 10 0.8526548 0.2720586 0.6779706 0.02376427 0.04267483 0.013589627
## 11 11 0.8592462 0.2588644 0.6826772 0.02854448 0.07390302 0.022964735
## 12 12 0.8511750 0.2745439 0.6765241 0.02288446 0.04185114 0.012393757
## 13 13 0.8517134 0.2736653 0.6773621 0.02284295 0.04132396 0.012437282
## 14 14 0.8521985 0.2728552 0.6776070 0.02318661 0.04172786 0.012554160
## 15 15 0.8521204 0.2729841 0.6782299 0.02311762 0.04136872 0.012525199
## 16 16 0.8523420 0.2726211 0.6782683 0.02257694 0.04062385 0.012477076
## 17 17 0.8653912 0.2493958 0.6887789 0.04530603 0.08285612 0.035032313
## 18 18 0.8664298 0.2488630 0.6896405 0.04811823 0.06585673 0.034646537
## 19 19 0.8539601 0.2700189 0.6798540 0.02316022 0.04233733 0.013175048
## 20 20 0.8668939 0.2480180 0.6902320 0.04819056 0.06663702 0.034311623
## 21 21 0.8642425 0.2511989 0.6878512 0.03221808 0.06676730 0.023687453
## 22 22 0.8537160 0.2705355 0.6801630 0.02327103 0.04243395 0.013064691
## 23 23 0.8637294 0.2521137 0.6879790 0.03798044 0.07290745 0.030833281
## 24 24 0.8542235 0.2696554 0.6802333 0.02293015 0.04221332 0.012719398
## 25 25 0.8541811 0.2696389 0.6799857 0.02282994 0.04205152 0.012089392
## 26 26 0.8546566 0.2688146 0.6802633 0.02266422 0.04151583 0.012269597
## 27 27 0.8767418 0.2290677 0.6982105 0.04882613 0.08418249 0.039214019
## 28 28 0.8788063 0.2256143 0.6990313 0.04618459 0.08269363 0.032353326
## 29 29 0.8749458 0.2322631 0.6962447 0.04783405 0.08304087 0.036207520
## 30 30 0.8549637 0.2683766 0.6804920 0.02198460 0.03959669 0.011451076
## 31 31 0.8647284 0.2503511 0.6882188 0.03679787 0.07053245 0.030238723
## 32 32 0.8546717 0.2688960 0.6797947 0.02187467 0.03931529 0.011185968
## 33 33 0.8772568 0.2281355 0.6988350 0.04989834 0.08679486 0.038415024
## 34 34 0.8603946 0.2573264 0.6854246 0.03873315 0.07314417 0.026485229
## 35 35 0.8649349 0.2498176 0.6884678 0.03862462 0.07464023 0.029474034
## 36 36 0.8675542 0.2463041 0.6904810 0.04197542 0.06670322 0.030614534
## 37 37 0.8554302 0.2677905 0.6804810 0.02235133 0.03924079 0.011918460
## 38 38 0.8654168 0.2493145 0.6882926 0.03608757 0.06885478 0.029096759
## 39 39 0.8617151 0.2552564 0.6864514 0.03832504 0.07213766 0.025485540
## 40 40 0.8560876 0.2667109 0.6809582 0.02188569 0.03824452 0.011161918
## 41 41 0.8684854 0.2449193 0.6911208 0.04138726 0.06536557 0.030047295
## 42 42 0.8882396 0.2095647 0.7077493 0.06897498 0.09635957 0.051397919
## 43 43 0.8754865 0.2306226 0.6957377 0.04003432 0.08116157 0.033498776
## 44 44 0.8565878 0.2658579 0.6810084 0.02118513 0.03748617 0.010770760
## 45 45 0.8565837 0.2659115 0.6808639 0.02129148 0.03732904 0.011219796
## 46 46 0.9013965 0.1848578 0.7171912 0.05442618 0.10156931 0.041251554
## 47 47 0.8572349 0.2649524 0.6813495 0.02193827 0.03803486 0.011240758
## 48 48 0.8793374 0.2255716 0.7002163 0.05321307 0.08223839 0.040584468
## 49 49 0.8579625 0.2637804 0.6819299 0.02213328 0.03902071 0.011584976
## 50 50 0.8778158 0.2269386 0.6984182 0.04410538 0.09494430 0.035665236
## 51 51 0.8811443 0.2223433 0.7001555 0.05659171 0.08653088 0.046050915
## 52 52 0.8671868 0.2461065 0.6886579 0.02719622 0.07034032 0.021697795
## 53 53 0.8689406 0.2435577 0.6906708 0.03709882 0.07136106 0.028048566
## 54 54 0.8709897 0.2404769 0.6907611 0.03277571 0.06702352 0.020138183
## 55 55 0.8996079 0.1877615 0.7154512 0.04942480 0.10112955 0.040712595
## 56 56 0.8600842 0.2604802 0.6831365 0.02339426 0.04060547 0.012128698
## 57 57 0.8603140 0.2601310 0.6832761 0.02334804 0.04049024 0.012183250
## 58 58 0.8601307 0.2603921 0.6833547 0.02304277 0.03938342 0.012017043
## 59 59 0.8696021 0.2425861 0.6915929 0.03692024 0.07061387 0.027400886
## 60 60 0.8608022 0.2593928 0.6840477 0.02331408 0.04024684 0.011631424
## 61 61 0.8604522 0.2599246 0.6839588 0.02300514 0.03863991 0.011557748
## 62 62 0.8806124 0.2233794 0.7000012 0.04704204 0.08145571 0.035789180
## 63 63 0.8773176 0.2280068 0.6969521 0.04248218 0.08470144 0.027793585
## 64 64 0.8810309 0.2224512 0.7013505 0.04940894 0.09177211 0.039758568
## 65 65 0.8670225 0.2471064 0.6902743 0.03788985 0.07092014 0.025237890
## 66 66 0.8731854 0.2383067 0.6946161 0.04618196 0.06178479 0.033096822
## 67 67 0.8728068 0.2378223 0.6924465 0.03215770 0.06565624 0.019210848
## 68 68 0.8815644 0.2212636 0.6996105 0.03961818 0.08065734 0.030111535
## 69 69 0.8618591 0.2578141 0.6853754 0.02322893 0.03919625 0.011619325
## 70 70 0.8884306 0.2082516 0.7071954 0.05812558 0.10235236 0.044693376
## 71 71 0.8936736 0.2005838 0.7097145 0.05150474 0.08428413 0.039397283
## 72 72 0.8826488 0.2195138 0.7009272 0.04034558 0.08307368 0.028684761
## 73 73 0.8708003 0.2410599 0.6928054 0.03601704 0.06774861 0.028430027
## 74 74 0.8851506 0.2165233 0.7055237 0.05289536 0.08900420 0.040310853
## 75 75 0.9024112 0.1828704 0.7169639 0.04490969 0.09784267 0.035139173
## 76 76 0.8894777 0.2072132 0.7078742 0.05854658 0.09423536 0.044278302
## 77 77 0.8852117 0.2172055 0.7057082 0.05654604 0.08646999 0.042327780
## 78 78 0.8736245 0.2371511 0.6962518 0.04348959 0.07789426 0.032355679
## 79 79 0.8823787 0.2212148 0.7026682 0.05160411 0.07807982 0.039953159
## 80 80 0.8705796 0.2411228 0.6924314 0.02865264 0.07198126 0.021020516
## 81 81 0.8877214 0.2129597 0.7070209 0.06026905 0.08419500 0.045819968
## 82 82 0.8718755 0.2393441 0.6944369 0.03626828 0.06934597 0.025941374
## 83 83 0.8838781 0.2183113 0.7031931 0.04361349 0.07608064 0.033025430
## 84 84 0.8913458 0.2036215 0.7108298 0.05214140 0.09958645 0.041422365
## 85 85 0.8773652 0.2282558 0.6980706 0.04139201 0.08212348 0.029008154
## 86 86 0.8840355 0.2179736 0.7043418 0.04808080 0.08303374 0.036310050
## 87 87 0.8762039 0.2336202 0.6972638 0.05188528 0.07222604 0.038792378
## 88 88 0.8927468 0.2016463 0.7101407 0.04708470 0.08580829 0.038592611
## 89 89 0.8888330 0.2074600 0.7055687 0.04292959 0.09088982 0.029545714
## 90 90 0.8722733 0.2389782 0.6932011 0.02982020 0.06136015 0.020894842
## 91 91 0.8722676 0.2388567 0.6947443 0.03616474 0.06992646 0.026323733
## 92 92 0.8725073 0.2384822 0.6948313 0.03620574 0.07002347 0.026360716
## 93 93 0.8875150 0.2135841 0.7073091 0.06046381 0.09261348 0.046440734
## 94 94 0.8731217 0.2375333 0.6951578 0.03652717 0.07044869 0.026364251
## 95 95 0.8768879 0.2326285 0.6978042 0.05140260 0.07157151 0.038677782
## 96 96 0.8759888 0.2337066 0.6975270 0.04194979 0.06672025 0.031038667
## 97 97 0.8640393 0.2548771 0.6874742 0.02277700 0.03935692 0.011976044
## 98 98 0.8772847 0.2320407 0.6979587 0.05146057 0.07165789 0.038611919
## 99 99 0.8641730 0.2546537 0.6876961 0.02286900 0.03913186 0.012114246
## 100 100 0.8803380 0.2231860 0.7004888 0.03750128 0.08669919 0.031974912
## 101 101 0.8641338 0.2547553 0.6875914 0.02320483 0.03957584 0.012739232
## 102 102 0.8728191 0.2382632 0.6947072 0.03573937 0.06738290 0.028659307
## 103 103 0.8642184 0.2546376 0.6876263 0.02306169 0.04005427 0.012564985
## 104 104 0.9070031 0.1769976 0.7212125 0.05680768 0.10416526 0.044090268
## 105 105 0.8851537 0.2169835 0.7031597 0.04760338 0.07369721 0.036095791
## 106 106 0.9070263 0.1767456 0.7194628 0.05292611 0.09101965 0.043005476
## 107 107 0.8867265 0.2151283 0.7065249 0.05616180 0.08627617 0.042537910
## 108 108 0.8639891 0.2550239 0.6873193 0.02239155 0.03952065 0.012436923
## 109 109 0.8801083 0.2235875 0.6999916 0.03732838 0.08676179 0.032212545
## 110 110 0.8807569 0.2239190 0.7019930 0.05197925 0.09541004 0.038088294
## 111 111 0.8851187 0.2169684 0.7031294 0.04834117 0.07473916 0.036552970
## 112 112 0.8770871 0.2325202 0.6975738 0.05116784 0.07140511 0.038656385
## 113 113 0.8640121 0.2550583 0.6870976 0.02267188 0.03967150 0.012443892
## 114 114 0.8745881 0.2360984 0.6961662 0.04291672 0.07719521 0.031900651
## 115 115 0.9046269 0.1803687 0.7171142 0.04564135 0.09138454 0.035804565
## 116 116 0.8642401 0.2546997 0.6869600 0.02285731 0.04003515 0.012614590
## 117 117 0.8771594 0.2325226 0.6972262 0.05146249 0.07178309 0.038760109
## 118 118 0.8854134 0.2162876 0.7046713 0.04836690 0.08278563 0.039307496
## 119 119 0.8894733 0.2104330 0.7062549 0.05318618 0.08343556 0.039362485
## 120 120 0.8899002 0.2084282 0.7077414 0.03730294 0.06927021 0.028990438
## 121 121 0.8746127 0.2366280 0.6963254 0.04035056 0.07173887 0.029864521
## 122 122 0.8885697 0.2107163 0.7061694 0.04328841 0.06687192 0.035712029
## 123 123 0.8740852 0.2376003 0.6958867 0.03518837 0.05516854 0.026241577
## 124 124 0.8834646 0.2207512 0.7036889 0.04878528 0.07343681 0.037630315
## 125 125 0.8794633 0.2267051 0.6996689 0.03563218 0.07082593 0.027703618
## 126 126 0.8925908 0.2043680 0.7111830 0.04447155 0.07234679 0.034471704
## 127 127 0.8748020 0.2360708 0.6952982 0.02827947 0.05825259 0.017053051
## 128 128 0.8648083 0.2539653 0.6878299 0.02274295 0.03996957 0.012564448
## 129 129 0.8826518 0.2220369 0.7014501 0.04281040 0.06379247 0.033517715
## 130 130 0.8646399 0.2542235 0.6877132 0.02276994 0.03971378 0.012588351
## 131 131 0.8646324 0.2542562 0.6876825 0.02265952 0.03963237 0.012480575
## 132 132 0.8715545 0.2412794 0.6917191 0.02498441 0.05130787 0.015678647
## 133 133 0.8677883 0.2477182 0.6902254 0.03028370 0.05546897 0.018425862
## 134 134 0.8718828 0.2408014 0.6918519 0.02504678 0.05087376 0.015695925
## 135 135 0.8823202 0.2242094 0.7024046 0.04863454 0.05807892 0.036998867
## 136 136 0.8737746 0.2382214 0.6944203 0.02550716 0.05249109 0.014349532
## 137 137 0.8799797 0.2266310 0.7002312 0.02858453 0.06111989 0.019149774
## 138 138 0.8734105 0.2394631 0.6950353 0.03807328 0.05064869 0.027661458
## 139 139 0.8783060 0.2294752 0.6988318 0.03755445 0.06952706 0.027737948
## 140 140 0.8686723 0.2459676 0.6907344 0.02372950 0.05524067 0.016065905
## 141 141 0.8738229 0.2386657 0.6954078 0.03993769 0.05426891 0.029654188
## 142 142 0.8752683 0.2344584 0.6964709 0.03942645 0.07236075 0.025807268
## 143 143 0.8649516 0.2538469 0.6880035 0.02270431 0.04005201 0.013137647
## 144 144 0.8648703 0.2539760 0.6878330 0.02257195 0.03984935 0.012952278
## 145 145 0.8648879 0.2539826 0.6878674 0.02269427 0.03980446 0.013020316
## 146 146 0.8971563 0.1966214 0.7117757 0.04212337 0.07059136 0.030432577
## 147 147 0.8804787 0.2241747 0.7014397 0.03683519 0.07228040 0.028419387
## 148 148 0.8824781 0.2240488 0.7024609 0.04906939 0.05893353 0.037169895
## 149 149 0.8650119 0.2538145 0.6879636 0.02272954 0.03980632 0.013364885
## 150 150 0.8678560 0.2476674 0.6903307 0.02997338 0.05539486 0.018564403
## 151 151 0.8817579 0.2244302 0.7009435 0.03169543 0.05593915 0.020879383
## 152 152 0.8803579 0.2264860 0.7010962 0.03557039 0.06039931 0.026105165
## 153 153 0.8735485 0.2387192 0.6943027 0.02477171 0.05122594 0.014086845
## 154 154 0.8649591 0.2539126 0.6880521 0.02267430 0.03951105 0.013467896
## 155 155 0.8715558 0.2414222 0.6916535 0.02534874 0.05140065 0.016252723
## 156 156 0.8929528 0.2038308 0.7099383 0.04733755 0.07722409 0.032561109
## 157 157 0.8790360 0.2289557 0.6995256 0.04027924 0.05820745 0.031580655
## 158 158 0.8649624 0.2539341 0.6879187 0.02268128 0.03974367 0.013448366
## 159 159 0.8651086 0.2537065 0.6880230 0.02268426 0.03953457 0.013395536
## 160 160 0.8909971 0.2087177 0.7087473 0.04250954 0.05994092 0.031013233
## 161 161 0.8650798 0.2537488 0.6879484 0.02287713 0.03961311 0.013614135
## 162 162 0.8736932 0.2385111 0.6942976 0.02525143 0.05196760 0.014542208
## 163 163 0.8855711 0.2163066 0.7030001 0.03454141 0.06206196 0.027363824
## 164 164 0.8726273 0.2403997 0.6937603 0.03547333 0.06311365 0.022867300
## 165 165 0.8736284 0.2392300 0.6950981 0.03882071 0.05116782 0.028632653
## 166 166 0.8648703 0.2540941 0.6878219 0.02286913 0.03969184 0.013376296
## 167 167 0.8648695 0.2540862 0.6877568 0.02278479 0.03963432 0.013323996
## 168 168 0.8809082 0.2254124 0.7009813 0.02937190 0.06206168 0.021008957
## 169 169 0.8723501 0.2405821 0.6944909 0.02985847 0.05675374 0.022179638
## 170 170 0.8818617 0.2229149 0.7016526 0.04377386 0.06734066 0.033431363
## 171 171 0.8733695 0.2396602 0.6946862 0.03788549 0.05004169 0.028037450
## 172 172 0.8676704 0.2480953 0.6899803 0.02931807 0.05448867 0.018336753
## 173 173 0.8851133 0.2166751 0.7029597 0.03248678 0.06836129 0.026951291
## 174 174 0.8650240 0.2538197 0.6877425 0.02226390 0.03927430 0.012854128
## 175 175 0.8890549 0.2118360 0.7080556 0.04709299 0.07365969 0.034832260
## 176 176 0.8805256 0.2246046 0.7004189 0.03748008 0.07199557 0.027582978
## 177 177 0.8647839 0.2542032 0.6874771 0.02235304 0.03954720 0.012816806
## 178 178 0.8718102 0.2411029 0.6914010 0.02587972 0.05273196 0.016710424
## 179 179 0.8731629 0.2393300 0.6937857 0.02479172 0.05149687 0.014703320
## 180 180 0.8811385 0.2255356 0.7015483 0.04301651 0.06414298 0.032972103
## 181 181 0.8842254 0.2193868 0.7033373 0.04528023 0.07647359 0.031189227
## 182 182 0.8716690 0.2413000 0.6913236 0.02606394 0.05312675 0.016698780
## 183 183 0.8672093 0.2487632 0.6894750 0.02891905 0.05390437 0.017452539
## 184 184 0.8689999 0.2455908 0.6906412 0.02409409 0.05715011 0.016854923
## 185 185 0.8715594 0.2414954 0.6912570 0.02616141 0.05329524 0.016724595
## 186 186 0.8719753 0.2411510 0.6940006 0.02941072 0.05627120 0.021643044
## 187 187 0.8731164 0.2394060 0.6938644 0.02505685 0.05200775 0.014892528
## 188 188 0.8645064 0.2546186 0.6873170 0.02236477 0.03934974 0.012832677
## 189 189 0.8886391 0.2120439 0.7064379 0.04178800 0.06562940 0.032115996
## 190 190 0.8733950 0.2395563 0.6949561 0.03906619 0.05154115 0.029433254
## 191 191 0.8733670 0.2395952 0.6949050 0.03910316 0.05151870 0.029465142
## 192 192 0.8691130 0.2454601 0.6907534 0.02446839 0.05780810 0.017321558
## 193 193 0.8644793 0.2546620 0.6872566 0.02249505 0.03924786 0.012886347
## 194 194 0.8643716 0.2548227 0.6871672 0.02260590 0.03920802 0.012994150
## 195 195 0.8643247 0.2549015 0.6871332 0.02267170 0.03927658 0.013057683
## 196 196 0.8643249 0.2549101 0.6871602 0.02264013 0.03930017 0.013028661
## 197 197 0.8642901 0.2549610 0.6871346 0.02264930 0.03936633 0.012982213
## 198 198 0.8732370 0.2392421 0.6940626 0.02573510 0.05260128 0.015841510
## 199 199 0.8738084 0.2387886 0.6949240 0.04165417 0.05611294 0.030488302
## 200 200 0.8716587 0.2413819 0.6914229 0.02632597 0.05303961 0.016934591
## 201 201 0.8644377 0.2547544 0.6872507 0.02266078 0.03939412 0.012980464
## 202 202 0.8741259 0.2358486 0.6934874 0.03130923 0.06314310 0.020291484
## 203 203 0.8785278 0.2294904 0.6986103 0.04156550 0.06779175 0.031666697
## 204 204 0.8644431 0.2547443 0.6872819 0.02269181 0.03937080 0.013009381
## 205 205 0.8823931 0.2237955 0.7017230 0.03950371 0.05865018 0.028991331
## 206 206 0.8734837 0.2392705 0.6947757 0.03483170 0.05335544 0.024946217
## 207 207 0.8644249 0.2547562 0.6872881 0.02264428 0.03931416 0.013062958
## 208 208 0.8793411 0.2284044 0.6992139 0.04065246 0.07322975 0.029898400
## 209 209 0.8670289 0.2490398 0.6895016 0.02949785 0.05441231 0.018298519
## 210 210 0.8733651 0.2394519 0.6946821 0.03435576 0.05269755 0.024477263
## 211 211 0.8644711 0.2546796 0.6872596 0.02260680 0.03933482 0.013000818
## 212 212 0.8708648 0.2429031 0.6922468 0.02968653 0.05484614 0.022292458
## 213 213 0.8644610 0.2546821 0.6872939 0.02257251 0.03919190 0.012980672
## 214 214 0.8740499 0.2384127 0.6951331 0.04219420 0.05675851 0.030928163
## 215 215 0.8644686 0.2546762 0.6873124 0.02253897 0.03916825 0.013026854
## 216 216 0.8733635 0.2391005 0.6942561 0.02591895 0.05278118 0.016029176
## 217 217 0.8819504 0.2244822 0.7011244 0.03744117 0.07088016 0.024705589
## 218 218 0.8723499 0.2406825 0.6945431 0.03087677 0.05795997 0.023571694
## 219 219 0.8736745 0.2391764 0.6950409 0.04018613 0.05264524 0.030095604
## 220 220 0.8643969 0.2547848 0.6872646 0.02247126 0.03908679 0.013036641
## 221 221 0.8740701 0.2386177 0.6952739 0.04110725 0.05373176 0.030621403
## 222 222 0.8711660 0.2426002 0.6923722 0.03030997 0.05542114 0.022546614
## 223 223 0.8644213 0.2547491 0.6873061 0.02250426 0.03907270 0.013040315
## 224 224 0.8644092 0.2547678 0.6872945 0.02251523 0.03907811 0.013057313
## 225 225 0.8731131 0.2394807 0.6940223 0.02543556 0.05197587 0.015377385
## 226 226 0.8644059 0.2547721 0.6872893 0.02252592 0.03906091 0.013054429
## 227 227 0.8900605 0.2103957 0.7065287 0.04782282 0.07383154 0.035485234
## 228 228 0.8820149 0.2242614 0.7017007 0.03402472 0.05854791 0.024620942
## 229 229 0.8717625 0.2412633 0.6916625 0.02649880 0.05329161 0.017515169
## 230 230 0.8820866 0.2242965 0.7023370 0.04544115 0.06741282 0.034691037
## 231 231 0.8742304 0.2384237 0.6954509 0.04159119 0.05420019 0.031139592
## 232 232 0.8815484 0.2250557 0.7007517 0.03672137 0.06972011 0.023924947
## 233 233 0.8734000 0.2394418 0.6950262 0.03473377 0.05307766 0.025707778
## 234 234 0.8760350 0.2337359 0.6974612 0.03879997 0.06324504 0.028274825
## 235 235 0.8898549 0.2089199 0.7091862 0.04649916 0.07989804 0.036512553
## 236 236 0.8969270 0.1974004 0.7123172 0.03575605 0.07587214 0.026061925
## 237 237 0.8798184 0.2274932 0.6990523 0.03058788 0.06173640 0.022624304
## 238 238 0.8957749 0.2008137 0.7138462 0.05773310 0.07092531 0.043826623
## 239 239 0.9186306 0.1599065 0.7301135 0.04597878 0.05408139 0.036537540
## 240 240 0.8644000 0.2547759 0.6873056 0.02253218 0.03909530 0.013066612
## nvmax
## 12 12
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Coefficients of final model:
## (Intercept) x4 x7 x9 x10
## -3.108145360 -0.001291464 0.338750221 0.099136004 0.037136438
## x16 x17 x21 stat14 stat98
## 0.026572248 0.033125681 0.004323330 -0.023488174 0.103541988
## stat110 stat144 sqrt.x18
## -0.095167667 0.018553690 0.799296053Test
if (algo.stepwise.caret == TRUE){
test.model(model.stepwise, data.test
,method = 'leapSeq',subopt = NULL
,formula = formula, feature.names = feature.names, label.names = label.names
,id = id
,draw.limits = TRUE, transformation = t)
}## [1] "Summary of predicted values: "
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -1.70939 -0.36589 -0.01430 -0.01661 0.36316 1.27519
## [1] "leapSeq Test MSE: 0.748371268905447"
Stepwise Selection with CV (w/ filtered train)
Train
Test
LASSO (w/ full train)
Train
if(algo.LASSO == TRUE){
# Formatting data for GLM net
# you can use model.matrix as well -- model.matrix creates a design (or model) matrix,
# e.g., by expanding factors to a set of dummy variables (depending on the contrasts)
# and expanding interactions similarly.
x = as.matrix(data.train[,feature.names])
y = data.train[,label.names]
xtest = as.matrix(data.test[,feature.names])
ytest = data.test[,label.names]
grid=10^seq(10,-2, length =100)
set.seed(1)
model.LASSO=glmnet(x,y,alpha=1, lambda =grid)
cv.out=cv.glmnet(x,y,alpha=1) # alpha=1 performs LASSO
plot(cv.out)
bestlambda<-cv.out$lambda.min # Optimal penalty parameter. You can make this call visually.
print(coef(model.LASSO,s=bestlambda))
}Test
if(algo.LASSO == TRUE){
lasso.pred=predict (model.LASSO ,s=bestlambda ,newx=xtest)
testMSE_LASSO = mean((ytest-lasso.pred)^2)
print (paste("LASSO Test RMSE: ",testMSE_LASSO, sep=""))
plot(ytest,lasso.pred)
}LASSO (w/ filtered train)
Train
if(algo.LASSO == TRUE){
# Formatting data for GLM net
# you can use model.matrix as well -- model.matrix creates a design (or model) matrix,
# e.g., by expanding factors to a set of dummy variables (depending on the contrasts)
# and expanding interactions similarly.
x = as.matrix(data.train2[,feature.names])
y = data.train2[,label.names]
xtest = as.matrix(data.test[,feature.names])
ytest = data.test[,label.names]
grid=10^seq(10,-2, length =100)
set.seed(1)
model.LASSO=glmnet(x,y,alpha=1, lambda =grid)
cv.out=cv.glmnet(x,y,alpha=1) # alpha=1 performs LASSO
plot(cv.out)
bestlambda<-cv.out$lambda.min # Optimal penalty parameter. You can make this call visually.
print(coef(model.LASSO,s=bestlambda))
}Test
if(algo.LASSO == TRUE){
lasso.pred=predict (model.LASSO ,s=bestlambda ,newx=xtest)
testMSE_LASSO = mean((ytest-lasso.pred)^2)
print (paste("LASSO Test RMSE: ",testMSE_LASSO, sep=""))
plot(ytest,lasso.pred)
}LASSO with CV (w/ full train)
Train
if (algo.LASSO.caret == TRUE){
set.seed(1)
returned = train.caret.glmselect(formula = formula
,data = data.train
,method = "glmnet"
,subopt = 'LASSO'
,feature.names = feature.names)
model.LASSO.caret = returned$model
}## Warning in nominalTrainWorkflow(x = x, y = y, wts = weights, info =
## trainInfo, : There were missing values in resampled performance measures.## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 1, lambda = 0.0152 on full training set
## glmnet
##
## 6002 samples
## 240 predictor
##
## No pre-processing
## Resampling: Cross-Validated (10 fold)
## Summary of sample sizes: 5402, 5401, 5402, 5401, 5402, 5402, ...
## Resampling results across tuning parameters:
##
## lambda RMSE Rsquared MAE
## 0.01000000 0.8532144 0.27102895 0.6795185
## 0.01047616 0.8530213 0.27141794 0.6794233
## 0.01097499 0.8528491 0.27177890 0.6793363
## 0.01149757 0.8526914 0.27212460 0.6792552
## 0.01204504 0.8525430 0.27246450 0.6791915
## 0.01261857 0.8524119 0.27278398 0.6791374
## 0.01321941 0.8522871 0.27310292 0.6790943
## 0.01384886 0.8521825 0.27339851 0.6790843
## 0.01450829 0.8521151 0.27364136 0.6791052
## 0.01519911 0.8520930 0.27381650 0.6791693
## 0.01592283 0.8521066 0.27394280 0.6792861
## 0.01668101 0.8521681 0.27399627 0.6794422
## 0.01747528 0.8522906 0.27395359 0.6796455
## 0.01830738 0.8524733 0.27382033 0.6799146
## 0.01917910 0.8527052 0.27361533 0.6802141
## 0.02009233 0.8529811 0.27334342 0.6805482
## 0.02104904 0.8532616 0.27307913 0.6808795
## 0.02205131 0.8535478 0.27281833 0.6812024
## 0.02310130 0.8538764 0.27248963 0.6815581
## 0.02420128 0.8542394 0.27211208 0.6819348
## 0.02535364 0.8546300 0.27170651 0.6823197
## 0.02656088 0.8550490 0.27127020 0.6827205
## 0.02782559 0.8554551 0.27088709 0.6831322
## 0.02915053 0.8559025 0.27045480 0.6835873
## 0.03053856 0.8563987 0.26995766 0.6840943
## 0.03199267 0.8569489 0.26938837 0.6846654
## 0.03351603 0.8575553 0.26874300 0.6852871
## 0.03511192 0.8582314 0.26799781 0.6859719
## 0.03678380 0.8589671 0.26716995 0.6867211
## 0.03853529 0.8597735 0.26623979 0.6875325
## 0.04037017 0.8605962 0.26531899 0.6883635
## 0.04229243 0.8614498 0.26437838 0.6892116
## 0.04430621 0.8623159 0.26346298 0.6900620
## 0.04641589 0.8632083 0.26254731 0.6909337
## 0.04862602 0.8641029 0.26169822 0.6917996
## 0.05094138 0.8650372 0.26083944 0.6927078
## 0.05336699 0.8659855 0.26003559 0.6936193
## 0.05590810 0.8669816 0.25921837 0.6945813
## 0.05857021 0.8679555 0.25856878 0.6955204
## 0.06135907 0.8689831 0.25791381 0.6965045
## 0.06428073 0.8700734 0.25724500 0.6975246
## 0.06734151 0.8712412 0.25653890 0.6986200
## 0.07054802 0.8725175 0.25573622 0.6997804
## 0.07390722 0.8739158 0.25481438 0.7010341
## 0.07742637 0.8754476 0.25375127 0.7023926
## 0.08111308 0.8771254 0.25252150 0.7038583
## 0.08497534 0.8789628 0.25109447 0.7054485
## 0.08902151 0.8809746 0.24943304 0.7071559
## 0.09326033 0.8831770 0.24749206 0.7089988
## 0.09770100 0.8855875 0.24521641 0.7110085
## 0.10235310 0.8882252 0.24253866 0.7131713
## 0.10722672 0.8911107 0.23937613 0.7155122
## 0.11233240 0.8941995 0.23583019 0.7180042
## 0.11768120 0.8975200 0.23177706 0.7206807
## 0.12328467 0.9008185 0.22798761 0.7233588
## 0.12915497 0.9041860 0.22418211 0.7260862
## 0.13530478 0.9077278 0.22008278 0.7289543
## 0.14174742 0.9115606 0.21522154 0.7320292
## 0.14849683 0.9157436 0.20928071 0.7353421
## 0.15556761 0.9203111 0.20197592 0.7389171
## 0.16297508 0.9250332 0.19399641 0.7425134
## 0.17073526 0.9299220 0.18502659 0.7461637
## 0.17886495 0.9344052 0.17741017 0.7494081
## 0.18738174 0.9386580 0.17034968 0.7524579
## 0.19630407 0.9425379 0.16487289 0.7552377
## 0.20565123 0.9464765 0.15917184 0.7580067
## 0.21544347 0.9507209 0.15192181 0.7610165
## 0.22570197 0.9550683 0.14334390 0.7640930
## 0.23644894 0.9584683 0.13963099 0.7665545
## 0.24770764 0.9614976 0.13858911 0.7687593
## 0.25950242 0.9646354 0.13833046 0.7710595
## 0.27185882 0.9680237 0.13833046 0.7735196
## 0.28480359 0.9717284 0.13833046 0.7761915
## 0.29836472 0.9757779 0.13833046 0.7791014
## 0.31257158 0.9802028 0.13833046 0.7822972
## 0.32745492 0.9850360 0.13833046 0.7858040
## 0.34304693 0.9903132 0.13833046 0.7896581
## 0.35938137 0.9960332 0.13311345 0.7938412
## 0.37649358 0.9982737 0.06590287 0.7954959
## 0.39442061 0.9984602 NaN 0.7956389
## 0.41320124 0.9984602 NaN 0.7956389
## 0.43287613 0.9984602 NaN 0.7956389
## 0.45348785 0.9984602 NaN 0.7956389
## 0.47508102 0.9984602 NaN 0.7956389
## 0.49770236 0.9984602 NaN 0.7956389
## 0.52140083 0.9984602 NaN 0.7956389
## 0.54622772 0.9984602 NaN 0.7956389
## 0.57223677 0.9984602 NaN 0.7956389
## 0.59948425 0.9984602 NaN 0.7956389
## 0.62802914 0.9984602 NaN 0.7956389
## 0.65793322 0.9984602 NaN 0.7956389
## 0.68926121 0.9984602 NaN 0.7956389
## 0.72208090 0.9984602 NaN 0.7956389
## 0.75646333 0.9984602 NaN 0.7956389
## 0.79248290 0.9984602 NaN 0.7956389
## 0.83021757 0.9984602 NaN 0.7956389
## 0.86974900 0.9984602 NaN 0.7956389
## 0.91116276 0.9984602 NaN 0.7956389
## 0.95454846 0.9984602 NaN 0.7956389
## 1.00000000 0.9984602 NaN 0.7956389
##
## Tuning parameter 'alpha' was held constant at a value of 1
## RMSE was used to select the optimal model using the smallest value.
## The final values used for the model were alpha = 1 and lambda = 0.01519911.
## alpha lambda
## 10 1 0.01519911
## alpha lambda RMSE Rsquared MAE RMSESD RsquaredSD
## 1 1 0.01000000 0.8532144 0.27102895 0.6795185 0.02213031 0.04153400
## 2 1 0.01047616 0.8530213 0.27141794 0.6794233 0.02212012 0.04162592
## 3 1 0.01097499 0.8528491 0.27177890 0.6793363 0.02211060 0.04169776
## 4 1 0.01149757 0.8526914 0.27212460 0.6792552 0.02209393 0.04175171
## 5 1 0.01204504 0.8525430 0.27246450 0.6791915 0.02207638 0.04180905
## 6 1 0.01261857 0.8524119 0.27278398 0.6791374 0.02204640 0.04185425
## 7 1 0.01321941 0.8522871 0.27310292 0.6790943 0.02201242 0.04191032
## 8 1 0.01384886 0.8521825 0.27339851 0.6790843 0.02197342 0.04196157
## 9 1 0.01450829 0.8521151 0.27364136 0.6791052 0.02192342 0.04198908
## 10 1 0.01519911 0.8520930 0.27381650 0.6791693 0.02186852 0.04203131
## 11 1 0.01592283 0.8521066 0.27394280 0.6792861 0.02180735 0.04210260
## 12 1 0.01668101 0.8521681 0.27399627 0.6794422 0.02173796 0.04218432
## 13 1 0.01747528 0.8522906 0.27395359 0.6796455 0.02168738 0.04223472
## 14 1 0.01830738 0.8524733 0.27382033 0.6799146 0.02167317 0.04234139
## 15 1 0.01917910 0.8527052 0.27361533 0.6802141 0.02167159 0.04248947
## 16 1 0.02009233 0.8529811 0.27334342 0.6805482 0.02165659 0.04261724
## 17 1 0.02104904 0.8532616 0.27307913 0.6808795 0.02164918 0.04274640
## 18 1 0.02205131 0.8535478 0.27281833 0.6812024 0.02162812 0.04282218
## 19 1 0.02310130 0.8538764 0.27248963 0.6815581 0.02158873 0.04284560
## 20 1 0.02420128 0.8542394 0.27211208 0.6819348 0.02152559 0.04280866
## 21 1 0.02535364 0.8546300 0.27170651 0.6823197 0.02144619 0.04275285
## 22 1 0.02656088 0.8550490 0.27127020 0.6827205 0.02137395 0.04266281
## 23 1 0.02782559 0.8554551 0.27088709 0.6831322 0.02129456 0.04264380
## 24 1 0.02915053 0.8559025 0.27045480 0.6835873 0.02123000 0.04264688
## 25 1 0.03053856 0.8563987 0.26995766 0.6840943 0.02116758 0.04264417
## 26 1 0.03199267 0.8569489 0.26938837 0.6846654 0.02109850 0.04264467
## 27 1 0.03351603 0.8575553 0.26874300 0.6852871 0.02101943 0.04266186
## 28 1 0.03511192 0.8582314 0.26799781 0.6859719 0.02092887 0.04267332
## 29 1 0.03678380 0.8589671 0.26716995 0.6867211 0.02082899 0.04267315
## 30 1 0.03853529 0.8597735 0.26623979 0.6875325 0.02072021 0.04266538
## 31 1 0.04037017 0.8605962 0.26531899 0.6883635 0.02061790 0.04266846
## 32 1 0.04229243 0.8614498 0.26437838 0.6892116 0.02050321 0.04260132
## 33 1 0.04430621 0.8623159 0.26346298 0.6900620 0.02039365 0.04255101
## 34 1 0.04641589 0.8632083 0.26254731 0.6909337 0.02030061 0.04241414
## 35 1 0.04862602 0.8641029 0.26169822 0.6917996 0.02019829 0.04231559
## 36 1 0.05094138 0.8650372 0.26083944 0.6927078 0.02009092 0.04215105
## 37 1 0.05336699 0.8659855 0.26003559 0.6936193 0.01996369 0.04203734
## 38 1 0.05590810 0.8669816 0.25921837 0.6945813 0.01987639 0.04188776
## 39 1 0.05857021 0.8679555 0.25856878 0.6955204 0.01978682 0.04192617
## 40 1 0.06135907 0.8689831 0.25791381 0.6965045 0.01973059 0.04199530
## 41 1 0.06428073 0.8700734 0.25724500 0.6975246 0.01967122 0.04212963
## 42 1 0.06734151 0.8712412 0.25653890 0.6986200 0.01962005 0.04223014
## 43 1 0.07054802 0.8725175 0.25573622 0.6997804 0.01956616 0.04231738
## 44 1 0.07390722 0.8739158 0.25481438 0.7010341 0.01951526 0.04240800
## 45 1 0.07742637 0.8754476 0.25375127 0.7023926 0.01946810 0.04250155
## 46 1 0.08111308 0.8771254 0.25252150 0.7038583 0.01942553 0.04259741
## 47 1 0.08497534 0.8789628 0.25109447 0.7054485 0.01938850 0.04269461
## 48 1 0.08902151 0.8809746 0.24943304 0.7071559 0.01935810 0.04279173
## 49 1 0.09326033 0.8831770 0.24749206 0.7089988 0.01933551 0.04288672
## 50 1 0.09770100 0.8855875 0.24521641 0.7110085 0.01932206 0.04297661
## 51 1 0.10235310 0.8882252 0.24253866 0.7131713 0.01931921 0.04305714
## 52 1 0.10722672 0.8911107 0.23937613 0.7155122 0.01932856 0.04312228
## 53 1 0.11233240 0.8941995 0.23583019 0.7180042 0.01933895 0.04326957
## 54 1 0.11768120 0.8975200 0.23177706 0.7206807 0.01940363 0.04330905
## 55 1 0.12328467 0.9008185 0.22798761 0.7233588 0.01950757 0.04376605
## 56 1 0.12915497 0.9041860 0.22418211 0.7260862 0.01977634 0.04402209
## 57 1 0.13530478 0.9077278 0.22008278 0.7289543 0.01998752 0.04451912
## 58 1 0.14174742 0.9115606 0.21522154 0.7320292 0.02021787 0.04494907
## 59 1 0.14849683 0.9157436 0.20928071 0.7353421 0.02045963 0.04526328
## 60 1 0.15556761 0.9203111 0.20197592 0.7389171 0.02071824 0.04543394
## 61 1 0.16297508 0.9250332 0.19399641 0.7425134 0.02084589 0.04595435
## 62 1 0.17073526 0.9299220 0.18502659 0.7461637 0.02105934 0.04541731
## 63 1 0.17886495 0.9344052 0.17741017 0.7494081 0.02112094 0.04583971
## 64 1 0.18738174 0.9386580 0.17034968 0.7524579 0.02146984 0.04486338
## 65 1 0.19630407 0.9425379 0.16487289 0.7552377 0.02157489 0.04507655
## 66 1 0.20565123 0.9464765 0.15917184 0.7580067 0.02186961 0.04500289
## 67 1 0.21544347 0.9507209 0.15192181 0.7610165 0.02220675 0.04445183
## 68 1 0.22570197 0.9550683 0.14334390 0.7640930 0.02269728 0.04189553
## 69 1 0.23644894 0.9584683 0.13963099 0.7665545 0.02269389 0.04230942
## 70 1 0.24770764 0.9614976 0.13858911 0.7687593 0.02310014 0.04208381
## 71 1 0.25950242 0.9646354 0.13833046 0.7710595 0.02352583 0.04198587
## 72 1 0.27185882 0.9680237 0.13833046 0.7735196 0.02400708 0.04198587
## 73 1 0.28480359 0.9717284 0.13833046 0.7761915 0.02453453 0.04198587
## 74 1 0.29836472 0.9757779 0.13833046 0.7791014 0.02511009 0.04198587
## 75 1 0.31257158 0.9802028 0.13833046 0.7822972 0.02573545 0.04198587
## 76 1 0.32745492 0.9850360 0.13833046 0.7858040 0.02641212 0.04198587
## 77 1 0.34304693 0.9903132 0.13833046 0.7896581 0.02714135 0.04198587
## 78 1 0.35938137 0.9960332 0.13311345 0.7938412 0.02785663 0.04095084
## 79 1 0.37649358 0.9982737 0.06590287 0.7954959 0.02648742 0.02007436
## 80 1 0.39442061 0.9984602 NaN 0.7956389 0.02634986 NA
## 81 1 0.41320124 0.9984602 NaN 0.7956389 0.02634986 NA
## 82 1 0.43287613 0.9984602 NaN 0.7956389 0.02634986 NA
## 83 1 0.45348785 0.9984602 NaN 0.7956389 0.02634986 NA
## 84 1 0.47508102 0.9984602 NaN 0.7956389 0.02634986 NA
## 85 1 0.49770236 0.9984602 NaN 0.7956389 0.02634986 NA
## 86 1 0.52140083 0.9984602 NaN 0.7956389 0.02634986 NA
## 87 1 0.54622772 0.9984602 NaN 0.7956389 0.02634986 NA
## 88 1 0.57223677 0.9984602 NaN 0.7956389 0.02634986 NA
## 89 1 0.59948425 0.9984602 NaN 0.7956389 0.02634986 NA
## 90 1 0.62802914 0.9984602 NaN 0.7956389 0.02634986 NA
## 91 1 0.65793322 0.9984602 NaN 0.7956389 0.02634986 NA
## 92 1 0.68926121 0.9984602 NaN 0.7956389 0.02634986 NA
## 93 1 0.72208090 0.9984602 NaN 0.7956389 0.02634986 NA
## 94 1 0.75646333 0.9984602 NaN 0.7956389 0.02634986 NA
## 95 1 0.79248290 0.9984602 NaN 0.7956389 0.02634986 NA
## 96 1 0.83021757 0.9984602 NaN 0.7956389 0.02634986 NA
## 97 1 0.86974900 0.9984602 NaN 0.7956389 0.02634986 NA
## 98 1 0.91116276 0.9984602 NaN 0.7956389 0.02634986 NA
## 99 1 0.95454846 0.9984602 NaN 0.7956389 0.02634986 NA
## 100 1 1.00000000 0.9984602 NaN 0.7956389 0.02634986 NA
## MAESD
## 1 0.01205254
## 2 0.01205634
## 3 0.01206334
## 4 0.01206718
## 5 0.01207186
## 6 0.01207773
## 7 0.01208212
## 8 0.01208962
## 9 0.01208539
## 10 0.01207119
## 11 0.01203982
## 12 0.01201413
## 13 0.01201180
## 14 0.01203452
## 15 0.01204081
## 16 0.01204296
## 17 0.01206584
## 18 0.01207545
## 19 0.01205847
## 20 0.01204280
## 21 0.01202169
## 22 0.01199355
## 23 0.01196173
## 24 0.01193148
## 25 0.01189170
## 26 0.01184633
## 27 0.01180142
## 28 0.01176667
## 29 0.01173400
## 30 0.01170072
## 31 0.01167052
## 32 0.01162271
## 33 0.01160952
## 34 0.01162639
## 35 0.01164193
## 36 0.01165852
## 37 0.01164774
## 38 0.01164345
## 39 0.01163413
## 40 0.01164999
## 41 0.01166904
## 42 0.01168990
## 43 0.01172180
## 44 0.01177201
## 45 0.01183517
## 46 0.01191829
## 47 0.01200229
## 48 0.01209508
## 49 0.01219115
## 50 0.01229927
## 51 0.01242821
## 52 0.01257477
## 53 0.01272817
## 54 0.01289412
## 55 0.01305209
## 56 0.01332566
## 57 0.01355870
## 58 0.01381813
## 59 0.01408067
## 60 0.01440225
## 61 0.01457181
## 62 0.01481270
## 63 0.01491671
## 64 0.01518372
## 65 0.01528243
## 66 0.01549299
## 67 0.01568406
## 68 0.01601445
## 69 0.01603721
## 70 0.01634352
## 71 0.01670172
## 72 0.01712025
## 73 0.01759827
## 74 0.01810894
## 75 0.01868027
## 76 0.01929644
## 77 0.01994855
## 78 0.02060290
## 79 0.01968956
## 80 0.01955552
## 81 0.01955552
## 82 0.01955552
## 83 0.01955552
## 84 0.01955552
## 85 0.01955552
## 86 0.01955552
## 87 0.01955552
## 88 0.01955552
## 89 0.01955552
## 90 0.01955552
## 91 0.01955552
## 92 0.01955552
## 93 0.01955552
## 94 0.01955552
## 95 0.01955552
## 96 0.01955552
## 97 0.01955552
## 98 0.01955552
## 99 0.01955552
## 100 0.01955552## Warning: Removed 21 rows containing missing values (geom_path).## Warning: Removed 21 rows containing missing values (geom_point).
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Test
if (algo.LASSO.caret == TRUE){
test.model(model.LASSO.caret, data.test
,method = 'glmnet',subopt = "LASSO"
,formula = formula, feature.names = feature.names, label.names = label.names
,draw.limits = TRUE, transformation = t)
}## [1] "Summary of predicted values: "
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -1.5867569 -0.3531352 0.0001909 -0.0149182 0.3386178 1.1951828
## [1] "glmnet LASSO Test MSE: 0.74645428329447"
LASSO with CV (w/ filtered train)
Train
if (algo.LASSO.caret == TRUE){
set.seed(1)
returned = train.caret.glmselect(formula = formula
,data = data.train2
,method = "glmnet"
,subopt = 'LASSO'
,feature.names = feature.names)
model.LASSO.caret = returned$model
}## Warning in nominalTrainWorkflow(x = x, y = y, wts = weights, info =
## trainInfo, : There were missing values in resampled performance measures.## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 1, lambda = 0.01 on full training set
## glmnet
##
## 5693 samples
## 240 predictor
##
## No pre-processing
## Resampling: Cross-Validated (10 fold)
## Summary of sample sizes: 5123, 5125, 5125, 5122, 5123, 5123, ...
## Resampling results across tuning parameters:
##
## lambda RMSE Rsquared MAE
## 0.01000000 0.7300569 0.3649212 0.5989470
## 0.01047616 0.7302125 0.3647215 0.5991038
## 0.01097499 0.7304015 0.3644722 0.5992906
## 0.01149757 0.7306117 0.3641943 0.5995038
## 0.01204504 0.7308470 0.3638825 0.5997293
## 0.01261857 0.7310757 0.3635931 0.5999373
## 0.01321941 0.7313365 0.3632562 0.6001735
## 0.01384886 0.7315600 0.3629976 0.6003751
## 0.01450829 0.7317989 0.3627257 0.6005734
## 0.01519911 0.7320198 0.3625013 0.6007537
## 0.01592283 0.7322642 0.3622516 0.6009675
## 0.01668101 0.7325225 0.3619914 0.6011949
## 0.01747528 0.7328274 0.3616612 0.6014686
## 0.01830738 0.7331612 0.3612923 0.6017843
## 0.01917910 0.7335341 0.3608673 0.6021273
## 0.02009233 0.7339601 0.3603558 0.6025325
## 0.02104904 0.7344365 0.3597694 0.6029860
## 0.02205131 0.7349152 0.3591965 0.6034332
## 0.02310130 0.7354272 0.3585835 0.6039151
## 0.02420128 0.7359679 0.3579346 0.6044235
## 0.02535364 0.7365525 0.3572235 0.6049864
## 0.02656088 0.7371686 0.3564785 0.6055701
## 0.02782559 0.7378325 0.3556712 0.6061807
## 0.02915053 0.7385434 0.3548051 0.6068127
## 0.03053856 0.7392840 0.3539129 0.6074618
## 0.03199267 0.7400448 0.3530193 0.6081146
## 0.03351603 0.7408342 0.3521128 0.6087878
## 0.03511192 0.7416617 0.3511713 0.6094999
## 0.03678380 0.7425517 0.3501481 0.6102623
## 0.03853529 0.7435223 0.3490135 0.6111047
## 0.04037017 0.7445429 0.3478377 0.6119780
## 0.04229243 0.7456316 0.3465842 0.6129086
## 0.04430621 0.7468028 0.3452200 0.6139100
## 0.04641589 0.7479522 0.3439597 0.6148943
## 0.04862602 0.7491489 0.3426746 0.6159141
## 0.05094138 0.7503254 0.3415079 0.6169250
## 0.05336699 0.7515307 0.3403765 0.6179445
## 0.05590810 0.7527827 0.3392382 0.6190027
## 0.05857021 0.7541126 0.3380358 0.6201250
## 0.06135907 0.7554713 0.3368956 0.6212821
## 0.06428073 0.7569274 0.3356700 0.6225258
## 0.06734151 0.7583942 0.3345730 0.6237695
## 0.07054802 0.7598934 0.3335815 0.6250207
## 0.07390722 0.7615125 0.3324947 0.6263446
## 0.07742637 0.7632734 0.3312751 0.6277701
## 0.08111308 0.7651959 0.3298812 0.6292954
## 0.08497534 0.7672998 0.3282698 0.6309341
## 0.08902151 0.7696020 0.3264006 0.6327200
## 0.09326033 0.7721204 0.3242251 0.6346454
## 0.09770100 0.7748747 0.3216845 0.6367355
## 0.10235310 0.7778860 0.3187071 0.6389863
## 0.10722672 0.7811773 0.3152051 0.6414367
## 0.11233240 0.7847733 0.3110712 0.6441249
## 0.11768120 0.7886211 0.3064013 0.6470015
## 0.12328467 0.7926498 0.3013986 0.6500002
## 0.12915497 0.7966175 0.2968251 0.6529685
## 0.13530478 0.8006686 0.2922789 0.6559816
## 0.14174742 0.8050114 0.2871043 0.6592268
## 0.14849683 0.8097453 0.2808167 0.6627025
## 0.15556761 0.8149085 0.2731105 0.6664801
## 0.16297508 0.8205372 0.2636238 0.6705628
## 0.17073526 0.8265182 0.2524695 0.6748672
## 0.17886495 0.8328677 0.2393182 0.6794349
## 0.18738174 0.8386213 0.2280075 0.6835365
## 0.19630407 0.8438772 0.2183184 0.6872162
## 0.20565123 0.8486249 0.2109336 0.6905271
## 0.21544347 0.8534394 0.2032721 0.6938726
## 0.22570197 0.8586048 0.1936368 0.6974712
## 0.23644894 0.8640819 0.1816624 0.7012540
## 0.24770764 0.8683351 0.1760412 0.7041914
## 0.25950242 0.8719189 0.1750832 0.7066458
## 0.27185882 0.8756840 0.1749919 0.7092422
## 0.28480359 0.8797818 0.1749919 0.7121225
## 0.29836472 0.8842572 0.1749919 0.7152929
## 0.31257158 0.8891430 0.1749919 0.7187959
## 0.32745492 0.8944743 0.1749919 0.7226130
## 0.34304693 0.9002889 0.1749919 0.7267961
## 0.35938137 0.9066275 0.1749919 0.7313103
## 0.37649358 0.9133391 0.1661875 0.7361134
## 0.39442061 0.9157676 NaN 0.7378530
## 0.41320124 0.9157676 NaN 0.7378530
## 0.43287613 0.9157676 NaN 0.7378530
## 0.45348785 0.9157676 NaN 0.7378530
## 0.47508102 0.9157676 NaN 0.7378530
## 0.49770236 0.9157676 NaN 0.7378530
## 0.52140083 0.9157676 NaN 0.7378530
## 0.54622772 0.9157676 NaN 0.7378530
## 0.57223677 0.9157676 NaN 0.7378530
## 0.59948425 0.9157676 NaN 0.7378530
## 0.62802914 0.9157676 NaN 0.7378530
## 0.65793322 0.9157676 NaN 0.7378530
## 0.68926121 0.9157676 NaN 0.7378530
## 0.72208090 0.9157676 NaN 0.7378530
## 0.75646333 0.9157676 NaN 0.7378530
## 0.79248290 0.9157676 NaN 0.7378530
## 0.83021757 0.9157676 NaN 0.7378530
## 0.86974900 0.9157676 NaN 0.7378530
## 0.91116276 0.9157676 NaN 0.7378530
## 0.95454846 0.9157676 NaN 0.7378530
## 1.00000000 0.9157676 NaN 0.7378530
##
## Tuning parameter 'alpha' was held constant at a value of 1
## RMSE was used to select the optimal model using the smallest value.
## The final values used for the model were alpha = 1 and lambda = 0.01.
## alpha lambda
## 1 1 0.01
## alpha lambda RMSE Rsquared MAE RMSESD RsquaredSD
## 1 1 0.01000000 0.7300569 0.3649212 0.5989470 0.02013901 0.01887969
## 2 1 0.01047616 0.7302125 0.3647215 0.5991038 0.02015218 0.01865761
## 3 1 0.01097499 0.7304015 0.3644722 0.5992906 0.02017753 0.01842683
## 4 1 0.01149757 0.7306117 0.3641943 0.5995038 0.02019906 0.01820390
## 5 1 0.01204504 0.7308470 0.3638825 0.5997293 0.02024497 0.01798917
## 6 1 0.01261857 0.7310757 0.3635931 0.5999373 0.02026158 0.01786115
## 7 1 0.01321941 0.7313365 0.3632562 0.6001735 0.02028413 0.01774860
## 8 1 0.01384886 0.7315600 0.3629976 0.6003751 0.02026013 0.01768358
## 9 1 0.01450829 0.7317989 0.3627257 0.6005734 0.02024008 0.01759055
## 10 1 0.01519911 0.7320198 0.3625013 0.6007537 0.02019186 0.01755504
## 11 1 0.01592283 0.7322642 0.3622516 0.6009675 0.02015790 0.01752877
## 12 1 0.01668101 0.7325225 0.3619914 0.6011949 0.02010608 0.01755861
## 13 1 0.01747528 0.7328274 0.3616612 0.6014686 0.02005514 0.01760403
## 14 1 0.01830738 0.7331612 0.3612923 0.6017843 0.01997867 0.01766266
## 15 1 0.01917910 0.7335341 0.3608673 0.6021273 0.01989168 0.01769709
## 16 1 0.02009233 0.7339601 0.3603558 0.6025325 0.01981395 0.01778449
## 17 1 0.02104904 0.7344365 0.3597694 0.6029860 0.01976759 0.01791081
## 18 1 0.02205131 0.7349152 0.3591965 0.6034332 0.01967640 0.01810086
## 19 1 0.02310130 0.7354272 0.3585835 0.6039151 0.01959774 0.01828333
## 20 1 0.02420128 0.7359679 0.3579346 0.6044235 0.01951109 0.01849900
## 21 1 0.02535364 0.7365525 0.3572235 0.6049864 0.01943450 0.01872238
## 22 1 0.02656088 0.7371686 0.3564785 0.6055701 0.01934744 0.01901354
## 23 1 0.02782559 0.7378325 0.3556712 0.6061807 0.01929076 0.01933112
## 24 1 0.02915053 0.7385434 0.3548051 0.6068127 0.01924031 0.01962039
## 25 1 0.03053856 0.7392840 0.3539129 0.6074618 0.01922121 0.01987854
## 26 1 0.03199267 0.7400448 0.3530193 0.6081146 0.01919621 0.02013961
## 27 1 0.03351603 0.7408342 0.3521128 0.6087878 0.01919127 0.02036903
## 28 1 0.03511192 0.7416617 0.3511713 0.6094999 0.01918628 0.02065708
## 29 1 0.03678380 0.7425517 0.3501481 0.6102623 0.01919729 0.02095487
## 30 1 0.03853529 0.7435223 0.3490135 0.6111047 0.01922335 0.02127278
## 31 1 0.04037017 0.7445429 0.3478377 0.6119780 0.01934032 0.02160920
## 32 1 0.04229243 0.7456316 0.3465842 0.6129086 0.01943594 0.02199059
## 33 1 0.04430621 0.7468028 0.3452200 0.6139100 0.01954013 0.02239037
## 34 1 0.04641589 0.7479522 0.3439597 0.6148943 0.01959391 0.02285037
## 35 1 0.04862602 0.7491489 0.3426746 0.6159141 0.01964524 0.02322746
## 36 1 0.05094138 0.7503254 0.3415079 0.6169250 0.01964515 0.02359057
## 37 1 0.05336699 0.7515307 0.3403765 0.6179445 0.01966784 0.02383247
## 38 1 0.05590810 0.7527827 0.3392382 0.6190027 0.01962915 0.02410810
## 39 1 0.05857021 0.7541126 0.3380358 0.6201250 0.01958542 0.02437795
## 40 1 0.06135907 0.7554713 0.3368956 0.6212821 0.01949745 0.02481481
## 41 1 0.06428073 0.7569274 0.3356700 0.6225258 0.01940228 0.02525891
## 42 1 0.06734151 0.7583942 0.3345730 0.6237695 0.01926723 0.02568710
## 43 1 0.07054802 0.7598934 0.3335815 0.6250207 0.01917844 0.02599666
## 44 1 0.07390722 0.7615125 0.3324947 0.6263446 0.01908423 0.02635145
## 45 1 0.07742637 0.7632734 0.3312751 0.6277701 0.01899289 0.02674255
## 46 1 0.08111308 0.7651959 0.3298812 0.6292954 0.01889941 0.02720296
## 47 1 0.08497534 0.7672998 0.3282698 0.6309341 0.01880447 0.02772249
## 48 1 0.08902151 0.7696020 0.3264006 0.6327200 0.01870870 0.02830884
## 49 1 0.09326033 0.7721204 0.3242251 0.6346454 0.01861246 0.02897114
## 50 1 0.09770100 0.7748747 0.3216845 0.6367355 0.01851622 0.02971954
## 51 1 0.10235310 0.7778860 0.3187071 0.6389863 0.01842050 0.03056511
## 52 1 0.10722672 0.7811773 0.3152051 0.6414367 0.01832593 0.03151958
## 53 1 0.11233240 0.7847733 0.3110712 0.6441249 0.01823324 0.03259489
## 54 1 0.11768120 0.7886211 0.3064013 0.6470015 0.01818317 0.03363465
## 55 1 0.12328467 0.7926498 0.3013986 0.6500002 0.01835225 0.03422372
## 56 1 0.12915497 0.7966175 0.2968251 0.6529685 0.01837719 0.03489991
## 57 1 0.13530478 0.8006686 0.2922789 0.6559816 0.01847591 0.03518739
## 58 1 0.14174742 0.8050114 0.2871043 0.6592268 0.01855694 0.03597420
## 59 1 0.14849683 0.8097453 0.2808167 0.6627025 0.01865265 0.03685698
## 60 1 0.15556761 0.8149085 0.2731105 0.6664801 0.01876904 0.03781767
## 61 1 0.16297508 0.8205372 0.2636238 0.6705628 0.01890877 0.03883585
## 62 1 0.17073526 0.8265182 0.2524695 0.6748672 0.01906913 0.03973329
## 63 1 0.17886495 0.8328677 0.2393182 0.6794349 0.01935394 0.04024461
## 64 1 0.18738174 0.8386213 0.2280075 0.6835365 0.01927362 0.04153130
## 65 1 0.19630407 0.8438772 0.2183184 0.6872162 0.01998430 0.03951080
## 66 1 0.20565123 0.8486249 0.2109336 0.6905271 0.02036327 0.04015452
## 67 1 0.21544347 0.8534394 0.2032721 0.6938726 0.02080244 0.04022799
## 68 1 0.22570197 0.8586048 0.1936368 0.6974712 0.02124193 0.04005153
## 69 1 0.23644894 0.8640819 0.1816624 0.7012540 0.02187787 0.03912107
## 70 1 0.24770764 0.8683351 0.1760412 0.7041914 0.02186196 0.04063697
## 71 1 0.25950242 0.8719189 0.1750832 0.7066458 0.02240949 0.03919870
## 72 1 0.27185882 0.8756840 0.1749919 0.7092422 0.02293721 0.03921811
## 73 1 0.28480359 0.8797818 0.1749919 0.7121225 0.02349154 0.03921811
## 74 1 0.29836472 0.8842572 0.1749919 0.7152929 0.02408241 0.03921811
## 75 1 0.31257158 0.8891430 0.1749919 0.7187959 0.02471116 0.03921811
## 76 1 0.32745492 0.8944743 0.1749919 0.7226130 0.02537908 0.03921811
## 77 1 0.34304693 0.9002889 0.1749919 0.7267961 0.02608736 0.03921811
## 78 1 0.35938137 0.9066275 0.1749919 0.7313103 0.02683709 0.03921811
## 79 1 0.37649358 0.9133391 0.1661875 0.7361134 0.02751735 0.02929608
## 80 1 0.39442061 0.9157676 NaN 0.7378530 0.02605539 NA
## 81 1 0.41320124 0.9157676 NaN 0.7378530 0.02605539 NA
## 82 1 0.43287613 0.9157676 NaN 0.7378530 0.02605539 NA
## 83 1 0.45348785 0.9157676 NaN 0.7378530 0.02605539 NA
## 84 1 0.47508102 0.9157676 NaN 0.7378530 0.02605539 NA
## 85 1 0.49770236 0.9157676 NaN 0.7378530 0.02605539 NA
## 86 1 0.52140083 0.9157676 NaN 0.7378530 0.02605539 NA
## 87 1 0.54622772 0.9157676 NaN 0.7378530 0.02605539 NA
## 88 1 0.57223677 0.9157676 NaN 0.7378530 0.02605539 NA
## 89 1 0.59948425 0.9157676 NaN 0.7378530 0.02605539 NA
## 90 1 0.62802914 0.9157676 NaN 0.7378530 0.02605539 NA
## 91 1 0.65793322 0.9157676 NaN 0.7378530 0.02605539 NA
## 92 1 0.68926121 0.9157676 NaN 0.7378530 0.02605539 NA
## 93 1 0.72208090 0.9157676 NaN 0.7378530 0.02605539 NA
## 94 1 0.75646333 0.9157676 NaN 0.7378530 0.02605539 NA
## 95 1 0.79248290 0.9157676 NaN 0.7378530 0.02605539 NA
## 96 1 0.83021757 0.9157676 NaN 0.7378530 0.02605539 NA
## 97 1 0.86974900 0.9157676 NaN 0.7378530 0.02605539 NA
## 98 1 0.91116276 0.9157676 NaN 0.7378530 0.02605539 NA
## 99 1 0.95454846 0.9157676 NaN 0.7378530 0.02605539 NA
## 100 1 1.00000000 0.9157676 NaN 0.7378530 0.02605539 NA
## MAESD
## 1 0.01913813
## 2 0.01907666
## 3 0.01902119
## 4 0.01895436
## 5 0.01890457
## 6 0.01882308
## 7 0.01875094
## 8 0.01865105
## 9 0.01857558
## 10 0.01847809
## 11 0.01838357
## 12 0.01829671
## 13 0.01820469
## 14 0.01809055
## 15 0.01796924
## 16 0.01785736
## 17 0.01776233
## 18 0.01761696
## 19 0.01747109
## 20 0.01731265
## 21 0.01713065
## 22 0.01693613
## 23 0.01676096
## 24 0.01661810
## 25 0.01652040
## 26 0.01642862
## 27 0.01635515
## 28 0.01626974
## 29 0.01620442
## 30 0.01616270
## 31 0.01620285
## 32 0.01621955
## 33 0.01624510
## 34 0.01624558
## 35 0.01623953
## 36 0.01618725
## 37 0.01613709
## 38 0.01605914
## 39 0.01598116
## 40 0.01588126
## 41 0.01577457
## 42 0.01561303
## 43 0.01547585
## 44 0.01533902
## 45 0.01520797
## 46 0.01505537
## 47 0.01488049
## 48 0.01471437
## 49 0.01454046
## 50 0.01432635
## 51 0.01409384
## 52 0.01384257
## 53 0.01361304
## 54 0.01340342
## 55 0.01335652
## 56 0.01321036
## 57 0.01310888
## 58 0.01298463
## 59 0.01286326
## 60 0.01274094
## 61 0.01260910
## 62 0.01246792
## 63 0.01244643
## 64 0.01219213
## 65 0.01253552
## 66 0.01266788
## 67 0.01281701
## 68 0.01296021
## 69 0.01326117
## 70 0.01313304
## 71 0.01347772
## 72 0.01379904
## 73 0.01417516
## 74 0.01461508
## 75 0.01510787
## 76 0.01562952
## 77 0.01619032
## 78 0.01678875
## 79 0.01728340
## 80 0.01626863
## 81 0.01626863
## 82 0.01626863
## 83 0.01626863
## 84 0.01626863
## 85 0.01626863
## 86 0.01626863
## 87 0.01626863
## 88 0.01626863
## 89 0.01626863
## 90 0.01626863
## 91 0.01626863
## 92 0.01626863
## 93 0.01626863
## 94 0.01626863
## 95 0.01626863
## 96 0.01626863
## 97 0.01626863
## 98 0.01626863
## 99 0.01626863
## 100 0.01626863## Warning: Removed 21 rows containing missing values (geom_path).## Warning: Removed 21 rows containing missing values (geom_point).
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Test
if (algo.LASSO.caret == TRUE){
test.model(model.LASSO.caret, data.test
,method = 'glmnet',subopt = "LASSO"
,formula = formula, feature.names = feature.names, label.names = label.names
,draw.limits = TRUE, transformation = t)
}## [1] "Summary of predicted values: "
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -1.92385 -0.43064 -0.05892 -0.06930 0.30571 1.33337
## [1] "glmnet LASSO Test MSE: 0.753377147868002"
LARS with CV (w/ full train)
Train
if (algo.LARS.caret == TRUE){
set.seed(1)
returned = train.caret.glmselect(formula = formula
,data = data.train
,method = "lars"
,subopt = 'NULL'
,feature.names = feature.names)
model.LARS.caret = returned$model
}## Warning in nominalTrainWorkflow(x = x, y = y, wts = weights, info =
## trainInfo, : There were missing values in resampled performance measures.## Aggregating results
## Selecting tuning parameters
## Fitting fraction = 0.404 on full training set
## Least Angle Regression
##
## 6002 samples
## 240 predictor
##
## Pre-processing: centered (240), scaled (240)
## Resampling: Cross-Validated (10 fold)
## Summary of sample sizes: 5402, 5401, 5402, 5401, 5402, 5402, ...
## Resampling results across tuning parameters:
##
## fraction RMSE Rsquared MAE
## 0.00000000 0.9984602 NaN 0.7956389
## 0.01010101 0.9853864 0.1383305 0.7860684
## 0.02020202 0.9735618 0.1383305 0.7775336
## 0.03030303 0.9630318 0.1383305 0.7699120
## 0.04040404 0.9540794 0.1451066 0.7634413
## 0.05050505 0.9457713 0.1600677 0.7575523
## 0.06060606 0.9381359 0.1705514 0.7521378
## 0.07070707 0.9311237 0.1825313 0.7470656
## 0.08080808 0.9245430 0.1942890 0.7421679
## 0.09090909 0.9181742 0.2053561 0.7372521
## 0.10101010 0.9121518 0.2142557 0.7325163
## 0.11111111 0.9064828 0.2213635 0.7279702
## 0.12121212 0.9012123 0.2271768 0.7237019
## 0.13131313 0.8963583 0.2328907 0.7197642
## 0.14141414 0.8916759 0.2386085 0.7160013
## 0.15151515 0.8872755 0.2434034 0.7124292
## 0.16161616 0.8831667 0.2473780 0.7090381
## 0.17171717 0.8793537 0.2506574 0.7058432
## 0.18181818 0.8758404 0.2533493 0.7028189
## 0.19191919 0.8726304 0.2555463 0.6999908
## 0.20202020 0.8697614 0.2573124 0.6973552
## 0.21212121 0.8672770 0.2589044 0.6949879
## 0.22222222 0.8650826 0.2607638 0.6928541
## 0.23232323 0.8631397 0.2625807 0.6909500
## 0.24242424 0.8614078 0.2643740 0.6892150
## 0.25252525 0.8598043 0.2661329 0.6875901
## 0.26262626 0.8583103 0.2678305 0.6860748
## 0.27272727 0.8570109 0.2692425 0.6847491
## 0.28282828 0.8560200 0.2702487 0.6837151
## 0.29292929 0.8552210 0.2710414 0.6829195
## 0.30303030 0.8545685 0.2717156 0.6822948
## 0.31313131 0.8540313 0.2723008 0.6817644
## 0.32323232 0.8536204 0.2727079 0.6813087
## 0.33333333 0.8532796 0.2730276 0.6809228
## 0.34343434 0.8529506 0.2733510 0.6805513
## 0.35353535 0.8526654 0.2736253 0.6801858
## 0.36363636 0.8524303 0.2738378 0.6798640
## 0.37373737 0.8522403 0.2739917 0.6795933
## 0.38383838 0.8521239 0.2740303 0.6793866
## 0.39393939 0.8520759 0.2739644 0.6792575
## 0.40404040 0.8520563 0.2738619 0.6791467
## 0.41414141 0.8520588 0.2737339 0.6790641
## 0.42424242 0.8520975 0.2735548 0.6790253
## 0.43434343 0.8521754 0.2733234 0.6790304
## 0.44444444 0.8522733 0.2730668 0.6790596
## 0.45454545 0.8523682 0.2728249 0.6790959
## 0.46464646 0.8524641 0.2725901 0.6791348
## 0.47474747 0.8525684 0.2723466 0.6791790
## 0.48484848 0.8526770 0.2721016 0.6792361
## 0.49494949 0.8527917 0.2718514 0.6792919
## 0.50505051 0.8529179 0.2715858 0.6793530
## 0.51515152 0.8530524 0.2713107 0.6794187
## 0.52525253 0.8531889 0.2710374 0.6794874
## 0.53535354 0.8533348 0.2707518 0.6795619
## 0.54545455 0.8534803 0.2704718 0.6796332
## 0.55555556 0.8536340 0.2701833 0.6797136
## 0.56565657 0.8537940 0.2698888 0.6797983
## 0.57575758 0.8539625 0.2695839 0.6798917
## 0.58585859 0.8541330 0.2692799 0.6799950
## 0.59595960 0.8543141 0.2689617 0.6801113
## 0.60606061 0.8545007 0.2686378 0.6802348
## 0.61616162 0.8546941 0.2683070 0.6803725
## 0.62626263 0.8548912 0.2679748 0.6805150
## 0.63636364 0.8550968 0.2676335 0.6806649
## 0.64646465 0.8553038 0.2672950 0.6808144
## 0.65656566 0.8555147 0.2669547 0.6809561
## 0.66666667 0.8557193 0.2666297 0.6810921
## 0.67676768 0.8559293 0.2662998 0.6812326
## 0.68686869 0.8561396 0.2659726 0.6813744
## 0.69696970 0.8563516 0.2656467 0.6815165
## 0.70707071 0.8565691 0.2653159 0.6816548
## 0.71717172 0.8567860 0.2649875 0.6817940
## 0.72727273 0.8569992 0.2646677 0.6819296
## 0.73737374 0.8572152 0.2643463 0.6820680
## 0.74747475 0.8574337 0.2640249 0.6822107
## 0.75757576 0.8576582 0.2636978 0.6823626
## 0.76767677 0.8578872 0.2633675 0.6825193
## 0.77777778 0.8581189 0.2630367 0.6826791
## 0.78787879 0.8583614 0.2626915 0.6828480
## 0.79797980 0.8586122 0.2623369 0.6830262
## 0.80808081 0.8588657 0.2619826 0.6832098
## 0.81818182 0.8591205 0.2616300 0.6833961
## 0.82828283 0.8593795 0.2612751 0.6835908
## 0.83838384 0.8596402 0.2609209 0.6837858
## 0.84848485 0.8599080 0.2605585 0.6839893
## 0.85858586 0.8601813 0.2601900 0.6841973
## 0.86868687 0.8604595 0.2598174 0.6844098
## 0.87878788 0.8607467 0.2594346 0.6846275
## 0.88888889 0.8610367 0.2590501 0.6848456
## 0.89898990 0.8613302 0.2586636 0.6850622
## 0.90909091 0.8616242 0.2582792 0.6852801
## 0.91919192 0.8619177 0.2578996 0.6854957
## 0.92929293 0.8622149 0.2575184 0.6857125
## 0.93939394 0.8625193 0.2571292 0.6859309
## 0.94949495 0.8628250 0.2567415 0.6861488
## 0.95959596 0.8631353 0.2563493 0.6863690
## 0.96969697 0.8634466 0.2559587 0.6865938
## 0.97979798 0.8637615 0.2555660 0.6868255
## 0.98989899 0.8640802 0.2551702 0.6870634
## 1.00000000 0.8644000 0.2547759 0.6873056
##
## RMSE was used to select the optimal model using the smallest value.
## The final value used for the model was fraction = 0.4040404.
## fraction
## 41 0.4040404## Warning: Removed 1 rows containing missing values (geom_point).
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Test
if (algo.LARS.caret == TRUE){
test.model(model.LARS.caret, data.test
,method = 'lars',subopt = NULL
,formula = formula, feature.names = feature.names, label.names = label.names
,draw.limits = TRUE, transformation = t)
}## [1] "Summary of predicted values: "
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -1.5858261 -0.3532425 -0.0002983 -0.0149202 0.3393275 1.1950036
## [1] "lars Test MSE: 0.746443158612502"
LARS with CV (w/ filtered train)
Train
if (algo.LARS.caret == TRUE){
set.seed(1)
returned = train.caret.glmselect(formula = formula
,data = data.train2
,method = "lars"
,subopt = 'NULL'
,feature.names = feature.names)
model.LARS.caret = returned$model
}## Warning in nominalTrainWorkflow(x = x, y = y, wts = weights, info =
## trainInfo, : There were missing values in resampled performance measures.## Aggregating results
## Selecting tuning parameters
## Fitting fraction = 0.636 on full training set
## Least Angle Regression
##
## 5693 samples
## 240 predictor
##
## Pre-processing: centered (240), scaled (240)
## Resampling: Cross-Validated (10 fold)
## Summary of sample sizes: 5123, 5125, 5125, 5122, 5123, 5123, ...
## Resampling results across tuning parameters:
##
## fraction RMSE Rsquared MAE
## 0.00000000 0.9157676 NaN 0.7378530
## 0.01010101 0.8998940 0.1749919 0.7264847
## 0.02020202 0.8855247 0.1749919 0.7161765
## 0.03030303 0.8727339 0.1749919 0.7071902
## 0.04040404 0.8616646 0.1865211 0.6996360
## 0.05050505 0.8513742 0.2065669 0.6924733
## 0.06060606 0.8420055 0.2207189 0.6859574
## 0.07070707 0.8333704 0.2378053 0.6798252
## 0.08080808 0.8250851 0.2550096 0.6738602
## 0.09090909 0.8171322 0.2694270 0.6681046
## 0.10101010 0.8096047 0.2809403 0.6625977
## 0.11111111 0.8025146 0.2900732 0.6573620
## 0.12121212 0.7959213 0.2973875 0.6524352
## 0.13131313 0.7898308 0.3047739 0.6478973
## 0.14141414 0.7839817 0.3120972 0.6435353
## 0.15151515 0.7784963 0.3181662 0.6394424
## 0.16161616 0.7733823 0.3231708 0.6356079
## 0.17171717 0.7686472 0.3272771 0.6319850
## 0.18181818 0.7642979 0.3306276 0.6285951
## 0.19191919 0.7603570 0.3333418 0.6254136
## 0.20202020 0.7569158 0.3357804 0.6225210
## 0.21212121 0.7538388 0.3383595 0.6198990
## 0.22222222 0.7510899 0.3408345 0.6175702
## 0.23232323 0.7486233 0.3433442 0.6154681
## 0.24242424 0.7463306 0.3459075 0.6134941
## 0.25252525 0.7441939 0.3483684 0.6116588
## 0.26262626 0.7422575 0.3506229 0.6099976
## 0.27272727 0.7406049 0.3524977 0.6085770
## 0.28282828 0.7392509 0.3540561 0.6074070
## 0.29292929 0.7380375 0.3555306 0.6063200
## 0.30303030 0.7370324 0.3567517 0.6053969
## 0.31313131 0.7362278 0.3577190 0.6046308
## 0.32323232 0.7355409 0.3585495 0.6039814
## 0.33333333 0.7349360 0.3592752 0.6034242
## 0.34343434 0.7343984 0.3599193 0.6029130
## 0.35353535 0.7338885 0.3605464 0.6024435
## 0.36363636 0.7334656 0.3610417 0.6020597
## 0.37373737 0.7331105 0.3614431 0.6017461
## 0.38383838 0.7327903 0.3618064 0.6014481
## 0.39393939 0.7325168 0.3621035 0.6011877
## 0.40404040 0.7322946 0.3623239 0.6009911
## 0.41414141 0.7321123 0.3624884 0.6008366
## 0.42424242 0.7319419 0.3626453 0.6006871
## 0.43434343 0.7317774 0.3628033 0.6005502
## 0.44444444 0.7316017 0.3629956 0.6004077
## 0.45454545 0.7314255 0.3631988 0.6002577
## 0.46464646 0.7312469 0.3634146 0.6001002
## 0.47474747 0.7310683 0.3636384 0.5999419
## 0.48484848 0.7308989 0.3638514 0.5997884
## 0.49494949 0.7307292 0.3640709 0.5996326
## 0.50505051 0.7305676 0.3642816 0.5994777
## 0.51515152 0.7304153 0.3644811 0.5993191
## 0.52525253 0.7302670 0.3646793 0.5991639
## 0.53535354 0.7301324 0.3648575 0.5990291
## 0.54545455 0.7300164 0.3650071 0.5989124
## 0.55555556 0.7299210 0.3651240 0.5988122
## 0.56565657 0.7298444 0.3652133 0.5987350
## 0.57575758 0.7297743 0.3652962 0.5986692
## 0.58585859 0.7297118 0.3653703 0.5986125
## 0.59595960 0.7296563 0.3654369 0.5985631
## 0.60606061 0.7296114 0.3654887 0.5985174
## 0.61616162 0.7295662 0.3655463 0.5984755
## 0.62626263 0.7295360 0.3655818 0.5984489
## 0.63636364 0.7295160 0.3656038 0.5984331
## 0.64646465 0.7295164 0.3655942 0.5984319
## 0.65656566 0.7295310 0.3655640 0.5984410
## 0.66666667 0.7295623 0.3655095 0.5984520
## 0.67676768 0.7296068 0.3654358 0.5984704
## 0.68686869 0.7296606 0.3653495 0.5984969
## 0.69696970 0.7297263 0.3652464 0.5985334
## 0.70707071 0.7298092 0.3651174 0.5985815
## 0.71717172 0.7299029 0.3649748 0.5986390
## 0.72727273 0.7300044 0.3648234 0.5987069
## 0.73737374 0.7301190 0.3646537 0.5987859
## 0.74747475 0.7302490 0.3644616 0.5988812
## 0.75757576 0.7303860 0.3642629 0.5989834
## 0.76767677 0.7305362 0.3640456 0.5990915
## 0.77777778 0.7306969 0.3638150 0.5992060
## 0.78787879 0.7308662 0.3635731 0.5993290
## 0.79797980 0.7310455 0.3633182 0.5994640
## 0.80808081 0.7312368 0.3630466 0.5996056
## 0.81818182 0.7314332 0.3627706 0.5997458
## 0.82828283 0.7316402 0.3624805 0.5999009
## 0.83838384 0.7318566 0.3621788 0.6000627
## 0.84848485 0.7320858 0.3618598 0.6002333
## 0.85858586 0.7323278 0.3615239 0.6004096
## 0.86868687 0.7325765 0.3611805 0.6005951
## 0.87878788 0.7328305 0.3608311 0.6007827
## 0.88888889 0.7330980 0.3604633 0.6009834
## 0.89898990 0.7333718 0.3600891 0.6011896
## 0.90909091 0.7336483 0.3597143 0.6014028
## 0.91919192 0.7339368 0.3593236 0.6016225
## 0.92929293 0.7342349 0.3589216 0.6018458
## 0.93939394 0.7345392 0.3585131 0.6020755
## 0.94949495 0.7348502 0.3580984 0.6023115
## 0.95959596 0.7351740 0.3576665 0.6025551
## 0.96969697 0.7355091 0.3572206 0.6028053
## 0.97979798 0.7358554 0.3567607 0.6030638
## 0.98989899 0.7362102 0.3562913 0.6033226
## 1.00000000 0.7365714 0.3558164 0.6035841
##
## RMSE was used to select the optimal model using the smallest value.
## The final value used for the model was fraction = 0.6363636.
## fraction
## 64 0.6363636## Warning: Removed 1 rows containing missing values (geom_point).
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Test
if (algo.LARS.caret == TRUE){
test.model(model.LARS.caret, data.test
,method = 'lars',subopt = NULL
,formula = formula, feature.names = feature.names, label.names = label.names
,draw.limits = TRUE, transformation = t)
}## [1] "Summary of predicted values: "
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -1.99969 -0.44259 -0.05504 -0.06924 0.31704 1.39209
## [1] "lars Test MSE: 0.756885383759297"
